diff --git a/.gitignore b/.gitignore
index 2bd9405687..0331420342 100644
--- a/.gitignore
+++ b/.gitignore
@@ -190,3 +190,7 @@ pufferlib/resources/drive/output*.gif
 emsdk/
 docs/book/*
 !docs/book/assets/
+
+logs/*
+
+eval_videos/*
diff --git a/README.md b/README.md
index 0b431e7169..513d5d6603 100644
--- a/README.md
+++ b/README.md
@@ -216,3 +216,14 @@ If you use PufferDrive in your research, please cite:
   year = {2025},
 }
 ```
+
+
+## Features
+
+### Verify human data
+
+Run
+```
+puffer verify puffer_drive --env.render-mode 0
+```
+with `control_mode` set to `expert_replay` or `inferred_`. Note: Currently only supported with classic dynamics model.
\ No newline at end of file
diff --git a/checkpoint_eval_results.csv b/checkpoint_eval_results.csv
new file mode 100644
index 0000000000..3a40ea9b67
--- /dev/null
+++ b/checkpoint_eval_results.csv
@@ -0,0 +1,12169 @@
+checkpoint,dataset,mode,scene_idx,n,score,collision_rate,at_fault_collision_rate,collisions_per_agent,offroad_rate,offroad_per_agent,completion_rate,episode_length,episode_return,perc_controlled,sp_maps,is_regularized,dynamics,anchor_maps
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diff --git a/eval_scaling_barplot.pdf b/eval_scaling_barplot.pdf
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diff --git a/eval_scaling_scatter.pdf b/eval_scaling_scatter.pdf
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diff --git a/eval_scaling_table.tex b/eval_scaling_table.tex
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@@ -0,0 +1,23 @@
+% Requires: \usepackage{booktabs}, \usepackage[table]{xcolor}, \usepackage{graphicx}, \usepackage{makecell}, \usepackage{bm}
+\begin{table}[ht]
+\centering
+\caption{Scaling evaluation results on held-out test maps.}
+\label{tab:scaling_results}
+\resizebox{\textwidth}{!}{%
+\begin{tabular}{rr|rrr|rrr}
+\toprule
+ & & \multicolumn{3}{c|}{Self-play (test)} & \multicolumn{3}{c}{Human-replay (test)} \\
+\makecell{Self-play maps \\ (metadata)} & Anchor data & Score & Coll. (\%) & At-fault (\%) & Score & Coll. (\%) & At-fault (\%) \\
+\midrule
+10 & 0 (unreg.) & \cellcolor{green!40} $0.865 \pm 0.018$ & \cellcolor{red!14} $5.9 \pm 1.4$ & \cellcolor{red!13} $5.0 \pm 1.2$ & \cellcolor{green!26} $0.683 \pm 0.015$ & \cellcolor{red!43} $23.4 \pm 1.3$ & \cellcolor{red!41} $22.8 \pm 1.3$ \\
+10 & 1.2 hours & \cellcolor{green!18} $0.589 \pm 0.026$ & \cellcolor{red!14} $6.1 \pm 1.5$ & \cellcolor{red!13} $5.0 \pm 1.4$ & \cellcolor{green!5} $0.420 \pm 0.015$ & \cellcolor{red!50} $27.7 \pm 1.4$ & \cellcolor{red!27} $13.7 \pm 1.1$ \\
+100 & 0 (unreg.) & \cellcolor{green!48} $0.957 \pm 0.010$ & \cellcolor{red!8} $2.3 \pm 0.8$ & \cellcolor{red!7} $1.7 \pm 0.5$ & \cellcolor{green!34} $0.783 \pm 0.013$ & \cellcolor{red!32} $16.9 \pm 1.2$ & \cellcolor{red!23} $11.1 \pm 1.0$ \\
+100 & 1.2 hours & \cellcolor{green!25} $0.674 \pm 0.024$ & \cellcolor{red!9} $3.0 \pm 1.1$ & \cellcolor{red!7} $1.4 \pm 0.7$ & \cellcolor{green!15} $0.547 \pm 0.016$ & \cellcolor{red!33} $17.9 \pm 1.2$ & \cellcolor{red!14} $5.9 \pm 0.7$ \\
+1k & 0 (unreg.) & \cellcolor{green!49} $0.972 \pm 0.009$ & \cellcolor{red!7} $1.8 \pm 0.9$ & \cellcolor{red!7} $1.3 \pm 0.7$ & \cellcolor{green!35} $0.799 \pm 0.013$ & \cellcolor{red!33} $17.6 \pm 1.2$ & \cellcolor{red!31} $16.1 \pm 1.1$ \\
+1k & 1.2 hours & \cellcolor{green!21} $0.627 \pm 0.025$ & \cellcolor{red!5} $0.3 \pm 0.3$ & \cellcolor{red!5} $0.1 \pm 0.1$ & \cellcolor{green!19} $0.604 \pm 0.015$ & \cellcolor{red!16} $7.3 \pm 0.8$ & \cellcolor{red!7} $1.9 \pm 0.4$ \\
+10k & 0 (unreg.) & \cellcolor{green!50} $\bm{0.978 \pm 0.009}$ & \cellcolor{red!6} $1.1 \pm 0.7$ & \cellcolor{red!5} $0.4 \pm 0.3$ & \cellcolor{green!40} $0.859 \pm 0.011$ & \cellcolor{red!16} $7.0 \pm 0.8$ & \cellcolor{red!15} $6.5 \pm 0.8$ \\
+50k & 0 (unreg.) & \cellcolor{green!49} $0.968 \pm 0.010$ & \cellcolor{red!7} $1.7 \pm 0.9$ & \cellcolor{red!7} $1.3 \pm 0.8$ & \cellcolor{green!42} $\bm{0.881 \pm 0.010}$ & \cellcolor{red!14} $6.2 \pm 0.8$ & \cellcolor{red!12} $4.5 \pm 0.6$ \\
+50k & 1.2 hours & \cellcolor{green!24} $0.666 \pm 0.023$ & \cellcolor{red!5} $\bm{0.2 \pm 0.1}$ & \cellcolor{red!5} $\bm{0.0 \pm 0.0}$ & \cellcolor{green!16} $0.560 \pm 0.016$ & \cellcolor{red!10} $\bm{3.1 \pm 0.5}$ & \cellcolor{red!7} $\bm{1.4 \pm 0.4}$ \\
+\bottomrule
+\end{tabular}}
+\end{table}
diff --git a/eval_scores.pdf b/eval_scores.pdf
new file mode 100644
index 0000000000..c8d2426ad1
Binary files /dev/null and b/eval_scores.pdf differ
diff --git a/examples/analyze.py b/examples/analyze.py
new file mode 100644
index 0000000000..ae079d448a
--- /dev/null
+++ b/examples/analyze.py
@@ -0,0 +1,768 @@
+"""Evaluate RL checkpoints across multiple eval modes.
+
+Modes:
+  1. Self-play on training maps
+  2. Self-play on validation maps
+  3. Human-replay on training maps
+  4. Human-replay on validation maps
+  5. Human-replay on interactive scenes (1k scenes selected for SDC interactivity)
+  6. Scaling analysis: all checkpoints in SCALING_CHECKPOINTS_PATH on validation
+     set in both self-play and human-replay modes
+
+When NUM_TOTAL_EVAL_AGENTS > NUM_AGENTS_PER_VECENV, we keep the buffer at
+NUM_AGENTS_PER_VECENV and loop resample_maps() to cover more scenes.
+
+Output: One row per scene per mode, with checkpoint name and metrics.
+
+Checkpoint naming convention (scaling):
+  [reg|unreg]_[dynamics]_[N]_maps[_anchor_[M]_maps].pt
+
+  Examples:
+    reg_delta_100_maps_anchor_50k_maps.pt
+      -> regularized, delta dynamics, 100 self-play maps, 50k anchor maps
+    unreg_delta_10_maps.pt
+      -> unregularized, delta dynamics, 10 self-play maps, no anchor
+
+Usage:
+    python evaluate_checkpoints.py
+"""
+
+import copy
+import os
+import re
+
+import numpy as np
+import pandas as pd
+
+from pufferlib.pufferl import load_env, load_policy, load_config
+from pufferlib.ocean.benchmark.evaluator_minimal import CheckpointEvaluator
+from pufferlib.ocean.benchmark.evaluator import WOSACEvaluator
+
+# ─── USER CONFIG ────────────────────────────────────────────────────────────────
+CHECKPOINTS = [
+    # "models/cpts_best/reg_delta_50k_maps_anchor_100_maps.pt"
+    # "models/rl/reg_self_play_50k.pt",
+]
+
+SCALING_CHECKPOINTS_PATH = "models/cpts_scaling"
+DETERMINISTIC = True
+
+TRAIN_MAP_DIR = "resources/drive/binaries/training_50k"
+VAL_MAP_DIR = "resources/drive/binaries/validation"  # 10k maps
+INTERACTIVE_MAP_DIR = "resources/drive/binaries/interactive_data_validation"
+NUM_TOTAL_EVAL_AGENTS = 1024 * 4  # * 4
+NUM_AGENTS_PER_VECENV = 1024  # 1024
+ENV_NAME = "puffer_drive"
+DATASET = "womd"
+OUTPUT_CSV = "checkpoint_eval_results.csv"
+WOSAC_OUTPUT_CSV = "checkpoint_wosac_results.csv"
+MAKE_FIGURES = True
+RUN_WOSAC = False
+WOSAC_ONLY = False
+RUN_RENDER = False
+
+# ─── VIDEO RENDERING CONFIG ─────────────────────────────────────────────────
+CHECKPOINTS_TO_RENDER = ["models/cpts_best/reg_delta_50k_maps_anchor_100_maps.pt"]
+NUM_ENVS_TO_RENDER = 3
+RENDER_MAP_DIR = INTERACTIVE_MAP_DIR  # Which maps to render on
+RENDER_NUM_MAPS = 100
+RENDER_OUTPUT_DIR = "eval_videos"
+RENDER_MODE = "worst_collision"  # "random" or "worst_collision"
+
+# WOSAC evaluation settings (aligned with run_wosac_eval_in_subprocess defaults)
+WOSAC_NUM_ROLLOUTS = 6
+WOSAC_TARGET_SCENARIOS = 512
+WOSAC_MAX_BATCHES = 1
+WOSAC_INIT_STEPS = 0
+WOSAC_INIT_MODE = "create_all_valid"
+WOSAC_CONTROL_MODE = "control_wosac"
+WOSAC_GOAL_BEHAVIOR = 2
+WOSAC_GOAL_RADIUS = 2.5
+WOSAC_SCENARIO_POOL_SIZE = 10_000
+# ────────────────────────────────────────────────────────────────────────────────
+
+METRICS = [
+    "n",
+    "score",
+    "collision_rate",
+    "at_fault_collision_rate",
+    "collisions_per_agent",
+    "offroad_rate",
+    "offroad_per_agent",
+    "completion_rate",
+    "episode_length",
+    "episode_return",
+    "perc_controlled",
+]
+
+
+def _parse_num(s):
+    """Parse a number string like '10', '1k', '50k' into an integer."""
+    m = re.match(r"(\d+)(k?)", s)
+    if not m:
+        return None
+    n = int(m.group(1))
+    if m.group(2) == "k":
+        n *= 1000
+    return n
+
+
+def make_eval_config(
+    base_config, map_dir, control_mode, goal_behavior=0, num_maps=50000, dynamics_model=None, is_regularized=False
+):
+    """Build an eval-ready config from the base config."""
+    config = copy.deepcopy(base_config)
+    # Map & agent setup
+    config["env"]["map_dir"] = map_dir
+    config["env"]["num_maps"] = num_maps
+    config["env"]["num_agents"] = NUM_AGENTS_PER_VECENV
+    # Dynamics — must be explicit to override .ini
+    if dynamics_model is not None:
+        config["env"]["dynamics_model"] = dynamics_model
+    # Action type
+    config["env"]["action_type"] = config["env"].get("action_type", "discrete")
+    # Rewards & conditioning
+    config["env"]["goal_radius"] = 2.5
+    config["env"]["goal_speed"] = 100.0
+    config["env"]["goal_behavior"] = goal_behavior
+    config["env"]["goal_target_distance"] = 30.0
+    config["env"]["fix_lambdas"] = True
+    config["env"]["fix_rewards"] = True
+    config["env"]["lambda_value"] = 0.15 if is_regularized else 0.0
+    config["env"]["reward_vehicle_collision"] = -1.0
+    config["env"]["reward_offroad_collision"] = -1.0
+    config["env"]["reward_goal"] = 1.0
+    # Noise
+    config["env"]["obs_partner_noise_speed"] = 0.0
+    config["env"]["obs_partner_noise_pos"] = 0.0
+    # Episode & termination
+    config["env"]["episode_length"] = 150
+    config["env"]["termination_mode"] = 1
+    config["env"]["dt"] = 0.1
+    # Control & init
+    config["env"]["control_mode"] = control_mode
+    config["env"]["init_mode"] = "create_all_valid"
+    config["env"]["init_steps"] = 0
+    config["env"]["max_controlled_agents"] = 32
+    # Behaviors
+    config["env"]["collision_behavior"] = 0
+    config["env"]["offroad_behavior"] = 0
+    # Regularization — disabled for eval
+    config["env"]["reg_mode"] = "None"
+    config["env"]["anchor_cpt_path"] = "None"
+    # Rendering — no rendering by default
+    config["env"]["render_mode"] = 1
+    # Resampling — disabled for eval
+    config["env"]["resample_frequency"] = 0
+    config["env"]["async_resets"] = False
+    # Vec config
+    config["vec"] = dict(backend="PufferEnv", num_envs=1)
+    return config
+
+
+def process_rollout_data(info_list, checkpoint, mode, scene_offset=0, dataset=DATASET):
+    """Return one dict per rollout (populated env log) with checkpoint/mode metadata."""
+
+    populated = [log for log in info_list if log and log.get("n", 0) > 0]
+    if not populated:
+        return []
+    rows = []
+    for i, log in enumerate(populated):
+        row = {
+            "checkpoint": checkpoint,
+            "dataset": dataset,
+            "mode": mode,
+            "scene_idx": scene_offset + i,  # TODO: Fix
+        }
+        for key in METRICS:
+            row[key] = float(log.get(key, 0.0))
+        rows.append(row)
+
+    return rows
+
+
+def num_resample_rounds():
+    """How many rollout rounds needed to cover NUM_TOTAL_EVAL_AGENTS."""
+    if NUM_TOTAL_EVAL_AGENTS <= NUM_AGENTS_PER_VECENV:
+        return 1
+    return (NUM_TOTAL_EVAL_AGENTS + NUM_AGENTS_PER_VECENV - 1) // NUM_AGENTS_PER_VECENV
+
+
+def run_mode(
+    evaluator, policy, base_config, map_dir, control_mode, checkpoint, mode_name, goal_behavior=0, num_maps=50000
+):
+    """Create env, rollout (with resampling if needed), collect per-scene rows, close env."""
+    config = make_eval_config(base_config, map_dir, control_mode, goal_behavior, num_maps)
+    env = load_env(ENV_NAME, config)
+    rows = []
+    n_rounds = num_resample_rounds()
+    print(n_rounds)
+
+    try:
+        for round_idx in range(n_rounds):
+            print(round_idx)
+            if round_idx > 0:
+                env.driver_env.resample_maps()
+
+            rollout_stats = evaluator.rollout(policy, env, deterministic=DETERMINISTIC)
+            scene_offset = round_idx * env.driver_env.num_envs
+            rows.extend(process_rollout_data(rollout_stats, checkpoint, mode_name, scene_offset))
+
+        n_scenes = len(rows)
+        if n_scenes > 0:
+            mean_score = np.mean([r["score"] for r in rows])
+            mean_coll = np.mean([r["collision_rate"] for r in rows])
+            mean_coll_fault = np.mean([r["at_fault_collision_rate"] for r in rows])
+            print(
+                f"  {mode_name}: {n_scenes} scenes, score={mean_score:.3f}, collision_rate={mean_coll:.3f}, at_fault_collision_rate={mean_coll_fault:.3f}"
+            )
+        else:
+            print(f"  {mode_name}: no populated scenes")
+    except Exception as e:
+        print(f"  {mode_name} failed (non-fatal): {e}")
+
+    env.close()
+    return rows
+
+
+def evaluate_checkpoint(checkpoint_path, base_config):
+    """Run all eval modes for a single checkpoint. Returns list of per-scene dicts."""
+
+    # Create first env before loading policy (load_policy needs vecenv.driver_env)
+    sp_train_config = make_eval_config(
+        base_config,
+        TRAIN_MAP_DIR,
+        control_mode="control_vehicles",
+        goal_behavior=0,
+    )
+    env = load_env(ENV_NAME, sp_train_config)
+
+    # load_policy reads load_model_path for checkpoint weights
+    base_config["load_model_path"] = checkpoint_path
+    policy = load_policy(base_config, env, ENV_NAME)
+    policy.eval()
+
+    evaluator = CheckpointEvaluator(base_config)
+    all_rows = []
+
+    # ── 1. Self-play on training maps (reuse the env we already created) ─────
+    n_rounds = num_resample_rounds()
+    try:
+        for round_idx in range(n_rounds):
+            if round_idx > 0:
+                env.driver_env.resample_maps()
+
+            info_list = evaluator.rollout(policy, env, deterministic=DETERMINISTIC)
+            scene_offset = round_idx * env.driver_env.num_envs
+            all_rows.extend(process_rollout_data(info_list, checkpoint_path, "sp_train", scene_offset))
+
+        sp_rows = [r for r in all_rows if r["mode"] == "sp_train"]
+        if sp_rows:
+            mean_score = np.mean([r["score"] for r in sp_rows])
+            mean_coll = np.mean([r["collision_rate"] for r in sp_rows])
+            print(f"  sp_train: {len(sp_rows)} scenes, score={mean_score:.3f}, collision_rate={mean_coll:.3f}")
+    except Exception as e:
+        print(f"  sp_train failed (non-fatal): {e}")
+    env.close()
+
+    # ── 2. Self-play on validation maps ──────────────────────────────────────
+    all_rows.extend(
+        run_mode(
+            evaluator, policy, base_config, VAL_MAP_DIR, "control_vehicles", checkpoint_path, "sp_val", num_maps=10_000
+        )
+    )
+
+    # ── 3. Human-replay on training maps ─────────────────────────────────────
+    all_rows.extend(
+        run_mode(
+            evaluator,
+            policy,
+            base_config,
+            TRAIN_MAP_DIR,
+            "control_sdc_only",
+            checkpoint_path,
+            "hr_train",
+            num_maps=50_000,
+        )
+    )
+
+    # ── 4. Human-replay on validation maps ───────────────────────────────────
+    all_rows.extend(
+        run_mode(
+            evaluator, policy, base_config, VAL_MAP_DIR, "control_sdc_only", checkpoint_path, "hr_val", num_maps=10_000
+        )
+    )
+
+    # ── 5. Human-replay on interactive scenes ────────────────────────────────
+    all_rows.extend(
+        run_mode(
+            evaluator,
+            policy,
+            base_config,
+            INTERACTIVE_MAP_DIR,
+            "control_sdc_only",
+            checkpoint_path,
+            "hr_interactive",
+            num_maps=100,
+        )
+    )
+
+    return all_rows
+
+
+def parse_scaling_checkpoint_name(filename):
+    """Parse a scaling checkpoint filename.
+
+    Format: [reg|unreg]_[dynamics]_[N]_maps[_anchor_[M]_maps].pt
+
+    Examples:
+        reg_delta_100_maps_anchor_50k_maps.pt  -> (100, True, "delta", 50000)
+        unreg_delta_10_maps.pt                 -> (10, False, "delta", None)
+
+    Returns:
+        (sp_maps: int, is_regularized: bool, dynamics: str, anchor_maps: int|None)
+        or None if parsing fails.
+    """
+    stem = filename.replace(".pt", "")
+
+    # Try regularized with anchor: reg_delta_100_maps_anchor_50k_maps
+    match = re.match(r"^(reg|unreg)_(\w+?)_(\d+k?)_maps_anchor_(\d+k?)_maps$", stem)
+    if match:
+        is_reg = match.group(1) == "reg"
+        dynamics = match.group(2)
+        sp_maps = _parse_num(match.group(3))
+        anchor_maps = _parse_num(match.group(4))
+        return sp_maps, is_reg, dynamics, anchor_maps
+
+    # Try without anchor: unreg_delta_10_maps
+    match = re.match(r"^(reg|unreg)_(\w+?)_(\d+k?)_maps$", stem)
+    if match:
+        is_reg = match.group(1) == "reg"
+        dynamics = match.group(2)
+        sp_maps = _parse_num(match.group(3))
+        return sp_maps, is_reg, dynamics, None
+
+    return None
+
+
+def evaluate_scaling_checkpoints(base_config):
+    scaling_entries = []
+
+    for fname in sorted(os.listdir(SCALING_CHECKPOINTS_PATH)):
+        if not fname.endswith(".pt"):
+            continue
+        parsed = parse_scaling_checkpoint_name(fname)
+        if parsed is None:
+            print(f"  Warning: could not parse scaling checkpoint name '{fname}', skipping")
+            continue
+
+        metadata_maps, is_reg, dynamics, anchor_maps = parsed
+        scaling_entries.append(
+            (
+                os.path.join(SCALING_CHECKPOINTS_PATH, fname),
+                metadata_maps,
+                is_reg,
+                dynamics,
+                anchor_maps,
+            )
+        )
+
+    scaling_entries.sort(key=lambda x: (x[1], x[2], x[4] or 0))
+    if not scaling_entries:
+        print("No scaling checkpoints found — skipping scaling eval.")
+        return []
+
+    print(f"\nFound {len(scaling_entries)} scaling checkpoints:")
+    for cpt_path, metadata_maps, is_reg, dynamics, anchor_maps in scaling_entries:
+        tag = "reg" if is_reg else "unreg"
+        anchor_str = f"anchor={anchor_maps}" if anchor_maps is not None else "no anchor"
+        print(f"  {os.path.basename(cpt_path)}  ->  sp={metadata_maps}, {tag}, {dynamics}, {anchor_str}")
+
+    DYNAMICS_CONFIG_MAP = {
+        "delta": "delta_local",
+        "classic": "classic",
+        "jerk": "jerk",
+    }
+
+    all_rows = []
+
+    for cpt_path, sp_maps, is_reg, dynamics, anchor_maps in scaling_entries:
+        print(f"\n{'─' * 60}")
+        print(f"Scaling eval: {os.path.basename(cpt_path)}")
+        print(f"{'─' * 60}")
+
+        dyn_config_name = DYNAMICS_CONFIG_MAP.get(dynamics, dynamics)
+        base_config["env"]["dynamics_model"] = dyn_config_name
+        base_config["load_model_path"] = cpt_path
+
+        sp_train_rows = []
+        sp_val_rows = []
+        hr_rows = []
+
+        # ── Self-play on training ────────────────────────────────────────
+        sp_train_config = make_eval_config(
+            base_config,
+            TRAIN_MAP_DIR,
+            control_mode="control_vehicles",
+            num_maps=50_000,
+            dynamics_model=dyn_config_name,
+            is_regularized=is_reg,
+        )
+        sp_train_config["env"]["dynamics_model"] = dyn_config_name
+        sp_train_env = load_env(ENV_NAME, sp_train_config)
+
+        policy = load_policy(base_config, sp_train_env, ENV_NAME)
+        policy.eval()
+        evaluator = CheckpointEvaluator(base_config)
+
+        sp_train_info = evaluator.rollout(policy, sp_train_env, deterministic=DETERMINISTIC)
+        sp_train_rows = process_rollout_data(sp_train_info, cpt_path, "scaling_sp_train")
+
+        if sp_train_rows:
+            mean_score = np.mean([r["score"] for r in sp_train_rows])
+            mean_coll = np.mean([r["collision_rate"] for r in sp_train_rows])
+            mean_offroad = np.mean([r["offroad_rate"] for r in sp_train_rows])
+            print(
+                f"  scaling_sp_train: {len(sp_train_rows)} scenes, score={mean_score:.3f}, collision_rate={mean_coll:.3f}, offroad_rate={mean_offroad:.3f}"
+            )
+
+        sp_train_env.close()
+
+        # ── Self-play on validation ──────────────────────────────────────
+        sp_val_config = make_eval_config(base_config, VAL_MAP_DIR, control_mode="control_vehicles", num_maps=10_000)
+        sp_val_config["env"]["dynamics_model"] = dyn_config_name
+        sp_val_env = load_env(ENV_NAME, sp_val_config)
+
+        policy = load_policy(base_config, sp_val_env, ENV_NAME)
+        policy.eval()
+        evaluator = CheckpointEvaluator(base_config)
+
+        sp_val_info = evaluator.rollout(policy, sp_val_env, deterministic=DETERMINISTIC)
+        sp_val_rows = process_rollout_data(sp_val_info, cpt_path, "scaling_sp_val")
+
+        if sp_val_rows:
+            mean_score = np.mean([r["score"] for r in sp_val_rows])
+            mean_coll = np.mean([r["collision_rate"] for r in sp_val_rows])
+            print(
+                f"  scaling_sp_val: {len(sp_val_rows)} scenes, score={mean_score:.3f}, collision_rate={mean_coll:.3f}"
+            )
+
+        sp_val_env.close()
+
+        # ── Human-replay on interactive scenes ───────────────────────────
+        hr_config = make_eval_config(base_config, INTERACTIVE_MAP_DIR, control_mode="control_sdc_only", num_maps=100)
+        hr_config["env"]["dynamics_model"] = dyn_config_name
+        hr_env = load_env(ENV_NAME, hr_config)
+
+        policy = load_policy(base_config, hr_env, ENV_NAME)
+        policy.eval()
+        evaluator = CheckpointEvaluator(base_config)
+
+        hr_info = evaluator.rollout(policy, hr_env, deterministic=DETERMINISTIC)
+        hr_rows = process_rollout_data(hr_info, cpt_path, "scaling_hr_interactive")
+
+        if hr_rows:
+            mean_score = np.mean([r["score"] for r in hr_rows])
+            mean_coll = np.mean([r["collision_rate"] for r in hr_rows])
+            mean_atfault_coll = np.mean([r["at_fault_collision_rate"] for r in hr_rows])
+            print(
+                f"  scaling_hr_interactive: {len(hr_rows)} scenes, score={mean_score:.3f}, collision_rate={mean_coll:.3f}, at_fault_collision_rate={mean_atfault_coll:.3f}"
+            )
+
+        hr_env.close()
+
+        # Attach scaling metadata to every row
+        for row in sp_train_rows + sp_val_rows + hr_rows:
+            row["sp_maps"] = sp_maps
+            row["is_regularized"] = is_reg
+            row["dynamics"] = dynamics
+            row["anchor_maps"] = anchor_maps
+
+        all_rows.extend(sp_train_rows)
+        all_rows.extend(sp_val_rows)
+        all_rows.extend(hr_rows)
+
+    return all_rows
+
+
+def make_wosac_config(base_config, map_dir, num_maps=None):
+    """Build a config suitable for WOSACEvaluator from the base config.
+
+    Settings aligned with run_wosac_eval_in_subprocess defaults.
+    """
+    num_maps = num_maps or WOSAC_SCENARIO_POOL_SIZE
+    config = copy.deepcopy(base_config)
+    config["env"]["map_dir"] = map_dir
+    config["env"]["num_maps"] = num_maps
+    config["env"]["num_agents"] = NUM_AGENTS_PER_VECENV
+    config["env"]["control_mode"] = WOSAC_CONTROL_MODE
+    config["env"]["goal_behavior"] = WOSAC_GOAL_BEHAVIOR
+    config["env"]["goal_radius"] = WOSAC_GOAL_RADIUS
+    config["env"]["init_mode"] = WOSAC_INIT_MODE
+    config["env"]["episode_length"] = 91
+    config["env"]["termination_mode"] = 1
+    config["env"]["fix_lambdas"] = True
+    config["env"]["fix_rewards"] = True
+    config["env"]["obs_partner_noise_speed"] = 0.0
+    config["env"]["obs_partner_noise_pos"] = 0.0
+    config["env"]["async_resets"] = False
+    config["vec"] = dict(backend="PufferEnv", num_envs=1)
+    config.setdefault("eval", {})
+    config["eval"]["wosac_num_rollouts"] = WOSAC_NUM_ROLLOUTS
+    config["eval"]["wosac_target_scenarios"] = WOSAC_TARGET_SCENARIOS
+    config["eval"]["wosac_max_batches"] = WOSAC_MAX_BATCHES
+    config["eval"]["wosac_init_steps"] = WOSAC_INIT_STEPS
+    config["eval"]["wosac_filter_out_post_done"] = True
+    config["eval"]["wosac_sanity_check"] = False
+    return config
+
+
+WOSAC_METRICS = [
+    "realism_meta_score",
+    "kinematic_metrics",
+    "interactive_metrics",
+    "map_based_metrics",
+]
+
+
+def evaluate_scaling_wosac(base_config):
+    """Run WOSAC evaluation for all scaling checkpoints on the validation set.
+
+    Returns:
+        pd.DataFrame with one row per scenario per checkpoint, containing
+        WOSAC metrics and scaling metadata columns.
+    """
+    scaling_entries = []
+    for fname in sorted(os.listdir(SCALING_CHECKPOINTS_PATH)):
+        if not fname.endswith(".pt"):
+            continue
+        parsed = parse_scaling_checkpoint_name(fname)
+        if parsed is None:
+            continue
+        sp_maps, is_reg, dynamics, anchor_maps = parsed
+        scaling_entries.append(
+            (
+                os.path.join(SCALING_CHECKPOINTS_PATH, fname),
+                sp_maps,
+                is_reg,
+                dynamics,
+                anchor_maps,
+            )
+        )
+
+    scaling_entries.sort(key=lambda x: (x[1], x[2], x[4] or 0))
+    if not scaling_entries:
+        print("No scaling checkpoints found — skipping WOSAC eval.")
+        return pd.DataFrame()
+
+    print(f"\nWOSAC evaluation for {len(scaling_entries)} scaling checkpoints:")
+
+    all_dfs = []
+    for cpt_path, sp_maps, is_reg, dynamics, anchor_maps in scaling_entries:
+        print(f"\n{'─' * 60}")
+        print(f"WOSAC eval: {os.path.basename(cpt_path)}")
+        print(f"{'─' * 60}")
+
+        wosac_config = make_wosac_config(base_config, VAL_MAP_DIR, num_maps=10_000)
+        env = load_env(ENV_NAME, wosac_config)
+        base_config["load_model_path"] = cpt_path
+        policy = load_policy(base_config, env, ENV_NAME)
+        policy.eval()
+
+        wosac_eval = WOSACEvaluator(wosac_config)
+        try:
+            df_scenes = wosac_eval.evaluate(wosac_config, env, policy, drop_scene_duplicates=True)
+
+            # Keep only the metrics we need, reset index to get scenario_id as column
+            df_scenes = df_scenes[WOSAC_METRICS].copy()
+            df_scenes = df_scenes.reset_index()
+            df_scenes["checkpoint"] = cpt_path
+            df_scenes["sp_maps"] = sp_maps
+            df_scenes["is_regularized"] = is_reg
+            df_scenes["dynamics"] = dynamics
+            df_scenes["anchor_maps"] = anchor_maps
+
+            n = len(df_scenes)
+            meta = df_scenes["realism_meta_score"].mean()
+            print(f"  WOSAC: {n} scenarios, realism_meta_score={meta:.4f}")
+            all_dfs.append(df_scenes)
+        except Exception as e:
+            print(f"  WOSAC failed (non-fatal): {e}")
+
+        env.close()
+
+    if not all_dfs:
+        return pd.DataFrame()
+    return pd.concat(all_dfs, ignore_index=True)
+
+
+def make_render_config(base_config, map_dir, num_maps=1000):
+    """Build a config for human-replay rendering with headless ffmpeg output."""
+    config = make_eval_config(
+        base_config,
+        map_dir,
+        control_mode="control_sdc_only",
+        goal_behavior=0,
+        num_maps=num_maps,
+    )
+    config["env"]["render_mode"] = 1  # Headless ffmpeg
+    return config
+
+
+def select_render_envs(evaluator, policy, env, num_to_render):
+    """Run a non-rendering rollout and return env indices with the worst collision rates.
+
+    Returns:
+        List of (env_idx, collision_rate) tuples, sorted by collision rate descending,
+        truncated to num_to_render.
+    """
+    info_list = evaluator.rollout(policy, env, deterministic=DETERMINISTIC)
+    populated = [log for log in info_list if log and log.get("n", 0) > 0]
+    did_collide = np.array([log["collision_rate"] for log in populated])
+
+    print(f"average collision rate: {did_collide.mean()}, n = {did_collide.shape[0]}")
+
+    scored = []
+    for env_idx, log in enumerate(info_list):
+        if not log or log.get("n", 0) <= 0:
+            continue
+        scored.append((env_idx, log.get("collision_rate", 0.0)))
+
+    scored.sort(key=lambda x: x[1], reverse=True)
+    return scored[:num_to_render]
+
+
+def render_checkpoint_videos(base_config):
+    """Render human-replay videos for each checkpoint in CHECKPOINTS_TO_RENDER.
+
+    Supports two modes (RENDER_MODE):
+      - "random": render the first NUM_ENVS_TO_RENDER env indices
+      - "worst_collision": run a stats-only rollout first, then render
+        the NUM_ENVS_TO_RENDER envs with the highest collision rates
+    """
+    import glob
+    import shutil
+
+    env = None
+
+    for cpt_path in CHECKPOINTS_TO_RENDER:
+        cpt_name = os.path.splitext(os.path.basename(cpt_path))[0]
+        cpt_video_dir = os.path.join(RENDER_OUTPUT_DIR, cpt_name)
+        os.makedirs(cpt_video_dir, exist_ok=True)
+
+        print(f"\n{'=' * 60}")
+        print(f"Rendering videos: {cpt_name}")
+        print(f"{'=' * 60}")
+
+        # Create env once (or reuse across checkpoints — same map config)
+        if env is None:
+            config = make_render_config(base_config, RENDER_MAP_DIR, num_maps=RENDER_NUM_MAPS)
+            env = load_env(ENV_NAME, config)
+
+        base_config["load_model_path"] = cpt_path
+        policy = load_policy(base_config, env, ENV_NAME)
+        policy.eval()
+
+        evaluator = CheckpointEvaluator(base_config)
+
+        # Select which envs to render
+        collision_rates = {}  # scenario_id -> collision_rate for filename tagging
+        if RENDER_MODE == "worst_collision":
+            print(f"  Running stats rollout to find worst collisions...")
+            selected = select_render_envs(evaluator, policy, env, NUM_ENVS_TO_RENDER)
+            env_indices = [idx for idx, _ in selected]
+            collision_rates = {idx: rate for idx, rate in selected}
+            for idx, coll_rate in selected:
+                print(f"    env {idx}: collision_rate={coll_rate:.3f}")
+        else:
+            env_indices = list(range(NUM_ENVS_TO_RENDER))
+
+        # Build env_idx -> scenario_id mapping for filename tagging
+        scenario_ids = env.driver_env.scenario_ids
+        idx_to_scenario = {idx: scenario_ids[idx].rstrip("\x00") for idx in env_indices}
+
+        # Run a stats rollout for "random" mode to get collision rates
+        if RENDER_MODE == "random" and not collision_rates:
+            info_list = evaluator.rollout(policy, env, deterministic=DETERMINISTIC)
+            for idx in env_indices:
+                if idx < len(info_list) and info_list[idx]:
+                    collision_rates[idx] = info_list[idx].get("collision_rate", 0.0)
+
+        # Map scenario_id -> collision_rate
+        scenario_collision = {}
+        for idx in env_indices:
+            sid = idx_to_scenario.get(idx, "")
+            scenario_collision[sid] = collision_rates.get(idx, 0.0)
+
+        # Render selected envs
+        for i, env_idx in enumerate(env_indices):
+            print(f"  Rendering env {env_idx} ({i + 1}/{len(env_indices)})...")
+            evaluator.rollout(policy, env, render_env_idx=env_idx, deterministic=True)
+            env.driver_env.stop_recorder(env_idx)
+
+        # Move mp4s into the checkpoint subdirectory, tagging with collision rate
+        for mp4_path in glob.glob("*.mp4"):
+            scenario_id = os.path.splitext(os.path.basename(mp4_path))[0]
+            coll_rate = scenario_collision.get(scenario_id, 0.0)
+            dest = os.path.join(cpt_video_dir, f"coll{coll_rate:.2f}_{scenario_id}.mp4")
+            shutil.move(mp4_path, dest)
+            print(f"  Saved: {dest}")
+
+    if env is not None:
+        env.close()
+
+    print(f"\nAll videos saved to {RENDER_OUTPUT_DIR}/")
+
+
+def main():
+    base_config = load_config(ENV_NAME)
+
+    df = pd.DataFrame()
+    wosac_df = pd.DataFrame()
+
+    if not WOSAC_ONLY:
+        all_rows = []
+        for cpt_path in CHECKPOINTS:
+            print(f"\n{'=' * 60}")
+            print(f"Evaluating: {cpt_path}")
+            print(f"{'=' * 60}")
+            rows = evaluate_checkpoint(cpt_path, base_config)
+            all_rows.extend(rows)
+
+        # ── Scaling analysis ─────────────────────────────────────────────────
+        scaling_rows = evaluate_scaling_checkpoints(base_config)
+        all_rows.extend(scaling_rows)
+
+        df = pd.DataFrame(all_rows)
+        df.to_csv(OUTPUT_CSV, index=False)
+        print(f"\nResults saved to {OUTPUT_CSV} ({len(df)} rows)")
+
+        if not df.empty:
+            summary = df.groupby(["checkpoint", "mode"])[["score", "collision_rate", "offroad_rate"]].mean()
+            print(f"\n{summary}")
+
+    # ── WOSAC scaling analysis ───────────────────────────────────────────────
+    if RUN_WOSAC:
+        wosac_df = evaluate_scaling_wosac(base_config)
+        if not wosac_df.empty:
+            wosac_df.to_csv(WOSAC_OUTPUT_CSV, index=False)
+            print(f"\nWOSAC results saved to {WOSAC_OUTPUT_CSV} ({len(wosac_df)} rows)")
+
+    # ── Figures ──────────────────────────────────────────────────────────────
+    if MAKE_FIGURES:
+        from pufferlib.ocean.benchmark.plot_and_format import make_all_figures
+
+        make_all_figures(
+            df if not df.empty else None,
+            wosac_df if not wosac_df.empty else None,
+        )
+
+    # ── Video rendering (last: env.close() may segfault in raylib cleanup) ──
+    if RUN_RENDER and CHECKPOINTS_TO_RENDER:
+        render_checkpoint_videos(base_config)
+
+    return df
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/download_data.py b/examples/download_data.py
new file mode 100644
index 0000000000..562836735f
--- /dev/null
+++ b/examples/download_data.py
@@ -0,0 +1,50 @@
+import os
+import zipfile
+import urllib.request
+
+BASE_URL = "https://huggingface.co/datasets/julianh65/pufferdrive_womd_subsets/resolve/main"
+
+FILES = [
+    "pufferdrive_womd_training_10k.zip",
+    "pufferdrive_womd_training_20k.zip",
+    "pufferdrive_womd_training_50k.zip",
+]
+OUT_DIR = "data/processed"
+
+os.makedirs(OUT_DIR, exist_ok=True)
+
+
+def make_progress_hook(filename):
+    def hook(block_num, block_size, total_size):
+        downloaded = block_num * block_size
+        if total_size > 0:
+            pct = min(downloaded / total_size * 100, 100)
+            mb_done = downloaded / 1e6
+            mb_total = total_size / 1e6
+            print(f"\r  {filename}: {pct:.1f}% ({mb_done:.1f} / {mb_total:.1f} MB)", end="", flush=True)
+        else:
+            print(f"\r  {filename}: {downloaded / 1e6:.1f} MB downloaded", end="", flush=True)
+
+    return hook
+
+
+for filename in FILES:
+    url = f"{BASE_URL}/{filename}"
+    zip_path = os.path.join(OUT_DIR, filename)
+
+    print(f"Downloading {filename}...")
+    urllib.request.urlretrieve(url, zip_path, reporthook=make_progress_hook(filename))
+    print()  # newline after progress line
+
+    print(f"Unzipping {filename}...")
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        members = zf.namelist()
+        for i, member in enumerate(members, 1):
+            zf.extract(member, OUT_DIR)
+            print(f"\r  {i}/{len(members)} files extracted", end="", flush=True)
+    print()
+
+    os.remove(zip_path)
+    print(f"Done: {filename}\n")
+
+print("All files downloaded and extracted.")
diff --git a/examples/eval_pipeline.py b/examples/eval_pipeline.py
new file mode 100644
index 0000000000..3a90687fac
--- /dev/null
+++ b/examples/eval_pipeline.py
@@ -0,0 +1,333 @@
+import numpy as np
+import pandas as pd
+import torch
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import seaborn as sns
+import warnings
+
+from pufferlib.pufferl import load_env, load_policy, load_config
+from pufferlib.ocean.benchmark.evaluator import WOSACEvaluator
+
+POLICY_DIR = "models"
+
+COLUMN_ORDER = [
+    "policy",
+    "realism_meta_score",
+    "kinematic_metrics",
+    "interactive_metrics",
+    "map_based_metrics",
+    "min_ade",
+    "ade",
+    "likelihood_linear_speed",
+    "likelihood_linear_acceleration",
+    "likelihood_angular_speed",
+    "likelihood_angular_acceleration",
+    "likelihood_collision_indication",
+    "likelihood_offroad_indication",
+    "likelihood_time_to_collision",
+    "likelihood_distance_to_road_edge",
+    "likelihood_distance_to_nearest_object",
+]
+
+
+def plot_wosac_results(df):
+    """Create a 3-column visualization of WOSAC results."""
+
+    # Set style
+    sns.set("notebook", font_scale=1.05, rc={"figure.figsize": (16, 5)})
+    sns.set_style("ticks", rc={"figure.facecolor": "none", "axes.facecolor": "none"})
+    warnings.filterwarnings("ignore")
+    plt.set_loglevel("WARNING")
+    mpl.rcParams["lines.markersize"] = 8
+
+    fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+    palette = sns.color_palette("Set2", n_colors=len(df["policy"].unique()))
+
+    # Left: Realism meta score
+    sns.barplot(data=df, x="policy", y="realism_meta_score", errorbar="sd", palette=palette, ax=axes[0], alpha=0.8)
+    axes[0].set_title("Aggregate realism score")
+    # axes[0].set_ylim(0, 1.0)
+    axes[0].grid(axis="y", alpha=0.3, linestyle="--")
+    axes[0].tick_params(axis="x", rotation=30)
+
+    # Middle: Metric Categories
+    df_metrics = df.melt(
+        id_vars=["policy"], value_vars=["kinematic_metrics", "interactive_metrics", "map_based_metrics"]
+    )
+    sns.barplot(
+        data=df_metrics, x="variable", y="value", hue="policy", errorbar="sd", palette=palette, ax=axes[1], alpha=0.8
+    )
+    axes[1].set_title("Group metric categories")
+    axes[1].set_xlabel("")
+    axes[1].set_ylabel("Score")
+    # axes[1].set_ylim(0, 1.0)
+    axes[1].legend(title="Policy", loc="upper left")
+    axes[1].grid(axis="y", alpha=0.3, linestyle="--")
+    axes[1].tick_params(axis="x", rotation=30)
+
+    # Right: ADE and minADE
+    df_ade = df.melt(id_vars=["policy"], value_vars=["ade", "min_ade"])
+    sns.barplot(
+        data=df_ade, x="policy", y="value", hue="variable", errorbar="sd", palette="muted", ax=axes[2], alpha=0.8
+    )
+    axes[2].set_title("Displacement error")
+    axes[2].set_ylabel("Distance (m)")
+    axes[2].legend(title="Metric")
+    axes[2].grid(axis="y", alpha=0.3, linestyle="--")
+    axes[2].tick_params(axis="x", rotation=15)
+
+    for ax in axes:
+        sns.despine(ax=ax)
+
+    plt.tight_layout()
+    plt.savefig("wosac_evaluation_results.png", dpi=300, bbox_inches="tight", facecolor="white")
+    plt.show()
+
+    return fig
+
+
+def plot_realism_score_distributions(df):
+    """Create histogram distributions of realism scores for each policy."""
+
+    # Set style
+    sns.set("notebook", font_scale=1.05)
+    sns.set_style("ticks", rc={"figure.facecolor": "none", "axes.facecolor": "none"})
+    warnings.filterwarnings("ignore")
+    plt.set_loglevel("WARNING")
+    mpl.rcParams["lines.markersize"] = 8
+
+    policies = df["policy"].unique()
+    n_policies = len(policies)
+
+    fig, axes = plt.subplots(1, n_policies, figsize=(5 * n_policies, 4), sharey=True, sharex=True)
+    if n_policies == 1:
+        axes = [axes]
+
+    palette = sns.color_palette("Set2", n_colors=n_policies)
+
+    for idx, (policy, ax) in enumerate(zip(policies, axes)):
+        policy_data = df[df["policy"] == policy]["realism_meta_score"]
+
+        # Plot histogram
+        ax.hist(policy_data, bins=20, alpha=0.8, color=palette[idx], edgecolor="black")
+
+        # Add mean line
+        mean_val = policy_data.mean()
+        ax.axvline(mean_val, color="red", linestyle="--", linewidth=2, label=f"Mean: {mean_val:.3f}")
+
+        # Add std text
+        std_val = policy_data.std()
+        ax.text(
+            0.05,
+            0.95,
+            f"μ = {mean_val:.3f}\nσ = {std_val:.3f}",
+            transform=ax.transAxes,
+            verticalalignment="top",
+            bbox=dict(boxstyle="round", facecolor="white", alpha=0.8),
+        )
+
+        ax.set_title(f"{policy}")
+        ax.set_xlabel("Realism meta score")
+        if idx == 0:
+            ax.set_ylabel("Count")
+        ax.grid(axis="y", alpha=0.3, linestyle="--")
+        ax.legend()
+        sns.despine(ax=ax)
+
+    plt.tight_layout()
+    plt.savefig("wosac_realism_score_distributions.png", dpi=300, bbox_inches="tight", facecolor="white")
+    plt.show()
+
+    return fig
+
+
+def evaluate_human_inferred_actions(config, vecenv, evaluator):
+    """Compute WOSAC metrics for human inferred actions."""
+
+    gt_trajectories = evaluator.collect_ground_truth_trajectories(vecenv)
+
+    vecenv._prep_human_data()
+
+    # Roll out inferred human actions in the simulator
+    simulated_trajectories = evaluator.collect_simulated_trajectories(
+        args=config,
+        puffer_env=vecenv,
+        policy=None,
+        actions=vecenv.expert_actions_discrete,
+    )
+
+    # Compute metrics
+    agent_state = vecenv.driver_env.get_global_agent_state()
+    road_edge_polylines = vecenv.driver_env.get_road_edge_polylines()
+    results = evaluator.compute_metrics(
+        gt_trajectories,
+        simulated_trajectories,
+        agent_state,
+        road_edge_polylines,
+    )
+
+    return results
+
+
+def evaluate_random_policy(config, vecenv, evaluator):
+    gt_trajectories = evaluator.collect_ground_truth_trajectories(vecenv)
+    simulated_trajectories = evaluator.collect_wosac_random_baseline(vecenv)
+
+    # Compute metrics
+    agent_state = vecenv.driver_env.get_global_agent_state()
+    road_edge_polylines = vecenv.driver_env.get_road_edge_polylines()
+    results = evaluator.compute_metrics(
+        gt_trajectories,
+        simulated_trajectories,
+        agent_state,
+        road_edge_polylines,
+    )
+
+    return results
+
+
+def evaluate_bc_policy(config, vecenv, evaluator, policy_path):
+    config["train"]["use_rnn"] = False
+    evaluator.mode = "bc_policy"
+
+    from train_bc_policy import BCPolicy
+
+    bc_policy = BCPolicy(
+        input_size=vecenv.observation_space.shape[-1],
+        hidden_size=1024,
+        output_size=21 * 31,
+    )
+    bc_policy.load_state_dict(torch.load(policy_path))
+    bc_policy.eval().to(config["train"]["device"])
+
+    gt_trajectories = evaluator.collect_ground_truth_trajectories(vecenv)
+    simulated_trajectories = evaluator.collect_simulated_trajectories(
+        args=config,
+        puffer_env=vecenv,
+        policy=bc_policy,
+    )
+
+    # Compute metrics
+    agent_state = vecenv.driver_env.get_global_agent_state()
+    road_edge_polylines = vecenv.driver_env.get_road_edge_polylines()
+    results = evaluator.compute_metrics(
+        gt_trajectories,
+        simulated_trajectories,
+        agent_state,
+        road_edge_polylines,
+    )
+
+    return results
+
+
+def evaluate_rl_policy(config, vecenv, evaluator, policy_path):
+    config["train"]["use_rnn"] = True
+    evaluator.mode = "rl_policy"
+
+    policy = load_policy(config, vecenv, "puffer_drive")
+
+    gt_trajectories = evaluator.collect_ground_truth_trajectories(vecenv)
+    simulated_trajectories = evaluator.collect_simulated_trajectories(config, vecenv, policy)
+
+    # Compute metrics
+    agent_state = vecenv.driver_env.get_global_agent_state()
+    road_edge_polylines = vecenv.driver_env.get_road_edge_polylines()
+    results = evaluator.compute_metrics(
+        gt_trajectories,
+        simulated_trajectories,
+        agent_state,
+        road_edge_polylines,
+    )
+    return results
+
+
+def pipeline(env_name="puffer_drive"):
+    """Obtain WOSAC scores for various baselines and policies."""
+
+    config = load_config(env_name)
+
+    # Dataset configuration
+    config["env"]["map_dir"] = "pufferlib/resources/drive/binaries/training"
+    config["eval"]["wosac_target_scenarios"] = 1000
+    config["eval"]["wosac_batch_size"] = 100
+    config["eval"]["wosac_scenario_pool_size"] = 10_0000
+
+    # WOSAC settings
+    config["wosac"]["enabled"] = True
+    config["vec"]["backend"] = "PufferEnv"
+    config["vec"]["num_envs"] = 1
+    config["env"]["init_mode"] = "create_all_valid"
+    config["env"]["control_mode"] = "control_wosac"
+    config["env"]["init_steps"] = 10
+    config["env"]["goal_behavior"] = 2
+    config["env"]["goal_radius"] = 1.0
+    config["env"]["save_data_to_disk"] = False
+
+    # Make env
+    vecenv = load_env(env_name, config)
+
+    # Make evaluator
+    evaluator = WOSACEvaluator(config)
+
+    # Baseline: Ground truth
+    evaluator.eval_mode = "ground_truth"
+    df_results_gt = evaluator.evaluate(config, vecenv, policy=None)
+    df_results_gt["policy"] = "ground_truth"
+
+    # Baseline: Agent with inferred human actions (using classic bicycle dynamics model)
+    # df_results_inferred_human = evaluate_human_inferred_actions(config, vecenv, evaluator)
+    # df_results_inferred_human["policy"] = "inferred_human_actions"
+
+    # Baseline: Imitation learning policy
+    # df_results_bc = evaluate_bc_policy(config, vecenv, evaluator, POLICY_DIR + "/bc_policy.pt")
+    # df_results_bc["policy"] = "bc_policy"
+
+    # Baseline: Self-play RL policy
+    # run: https://wandb.ai/emerge_/gsp/runs/qld2z6tn?nw=nwuserdaphnecor
+    # df_results_self_play = evaluate_rl_policy(
+    #     config, vecenv, evaluator, "pufferlib/resources/drive/pufferdrive_weights.pt"
+    # )  # POLICY_DIR + "/puffer_drive_sp_qld2z6tn.pt")
+    # df_results_self_play["policy"] = "self_play_rl_base"
+
+    # TODO: Guided self-play policy (guidance in rewards)
+    # ...
+
+    # TODO: Guided self-play policy (regularization)
+    # ...
+
+    # Baseline: Random policy
+    # df_results_random = evaluate_random_policy(config, vecenv, evaluator)
+    # df_results_random["policy"] = "random"
+
+    # Combine
+    df = pd.concat(
+        [
+            df_results_gt,
+            # df_results_inferred_human,
+            # df_results_random,
+            # df_results_bc,
+            # df_results_self_play,
+        ],
+        ignore_index=True,
+    )
+
+    df = df[COLUMN_ORDER]
+
+    # Visualize
+    plot_wosac_results(df)
+    plot_realism_score_distributions(df)
+
+    print(f"total agents: {df_results_gt['num_agents_per_scene'].sum().item()}")
+
+    print(df.groupby("policy")["realism_meta_score"].mean())
+    print("---")
+    print(df.groupby("policy")["kinematic_metrics"].mean())
+    print("---")
+    print(df.groupby("policy")["interactive_metrics"].mean())
+    print("---")
+    print(df.groupby("policy")["map_based_metrics"].mean())
+
+
+if __name__ == "__main__":
+    pipeline()
diff --git a/examples/find_interactive_scenes.py b/examples/find_interactive_scenes.py
new file mode 100644
index 0000000000..6a8c363927
--- /dev/null
+++ b/examples/find_interactive_scenes.py
@@ -0,0 +1,618 @@
+"""
+Find and filter driving scenarios by interactivity.
+
+Interactivity proxy: number of times the SDC trajectory intersects
+with any other agent's trajectory (line-segment intersections).
+
+Pipeline:
+  1. Score every scenario JSON in --data_folder
+  2. Save a CSV/parquet dataframe with per-scenario intersection counts
+  3. Copy the top-K most interactive JSONs into --output_folder
+  4. (Optional) --visualize: render plots of the top-K scenarios into --plot_folder
+
+Usage:
+    # Basic: score all, copy top 500, save dataframe
+    python examples/find_interactive_scenes.py \
+        --data_folder data/processed/training \
+        --top_k 500
+
+    # With visualization of top 50
+    python examples/find_interactive_scenes.py \
+        --data_folder data/processed/training \
+        --top_k 100 \
+        --visualize \
+        --vis_top_k 50
+        --prioritize_intersections
+
+    # Cap file count for quick testing
+    python examples/find_interactive_scenes.py \
+        --data_folder data/processed/training_50k \
+        --top_k 1000 \
+        --visualize \
+        --vis_top_k 50 \
+"""
+
+import json
+import shutil
+import argparse
+import math
+import warnings
+from pathlib import Path
+from multiprocessing import Pool, cpu_count
+
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+import matplotlib as mpl
+
+mpl.use("Agg")
+import matplotlib.pyplot as plt
+import matplotlib.collections as mc
+import seaborn as sns
+
+
+# ── geometry helpers ─────────────────────────────────────────────────────────
+
+
+def ccw(ax, ay, bx, by, cx, cy):
+    return (cy - ay) * (bx - ax) > (by - ay) * (cx - ax)
+
+
+def segments_intersect(ax, ay, bx, by, cx, cy, dx, dy):
+    """Return True if segment AB intersects segment CD."""
+    if ccw(ax, ay, cx, cy, dx, dy) != ccw(bx, by, cx, cy, dx, dy) and ccw(ax, ay, bx, by, cx, cy) != ccw(
+        ax, ay, bx, by, dx, dy
+    ):
+        return True
+    return False
+
+
+def segment_intersection_point(ax, ay, bx, by, cx, cy, dx, dy):
+    """Return (x, y) of intersection between AB and CD, or None."""
+    denom = (ax - bx) * (cy - dy) - (ay - by) * (cx - dx)
+    if abs(denom) < 1e-12:
+        return None
+    t = ((ax - cx) * (cy - dy) - (ay - cy) * (cx - dx)) / denom
+    ix = ax + t * (bx - ax)
+    iy = ay + t * (by - ay)
+    return (ix, iy)
+
+
+def extract_trajectory(obj):
+    """Return list of (x, y) for valid timesteps, None for gaps."""
+    positions = obj.get("position", [])
+    valids = obj.get("valid", [])
+    pts = []
+    for i, pos in enumerate(positions):
+        if i < len(valids) and valids[i]:
+            pts.append((pos.get("x", 0.0), pos.get("y", 0.0)))
+        else:
+            pts.append(None)
+    return pts
+
+
+def traj_to_segments(traj):
+    """Convert trajectory to list of ((x0,y0),(x1,y1)) segments, skipping gaps."""
+    segs = []
+    for i in range(len(traj) - 1):
+        if traj[i] is not None and traj[i + 1] is not None:
+            segs.append((traj[i], traj[i + 1]))
+    return segs
+
+
+def segment_angle(ax, ay, bx, by, cx, cy, dx, dy):
+    """Return the acute angle (radians) between segments AB and CD."""
+    # Direction vectors
+    ux, uy = bx - ax, by - ay
+    vx, vy = dx - cx, dy - cy
+    dot = ux * vx + uy * vy
+    mag_u = math.sqrt(ux * ux + uy * uy)
+    mag_v = math.sqrt(vx * vx + vy * vy)
+    if mag_u < 1e-9 or mag_v < 1e-9:
+        return 0.0
+    cos_theta = max(-1.0, min(1.0, dot / (mag_u * mag_v)))
+    angle = math.acos(abs(cos_theta))  # acute angle in [0, pi/2]
+    return angle
+
+
+def score_intersections(traj_a, traj_b, angle_threshold_rad=0.0):
+    """Score intersections between two trajectories.
+
+    Returns:
+        raw_count:      total segment-segment intersections
+        angled_count:   intersections where the acute angle >= angle_threshold_rad
+    """
+    segs_a = traj_to_segments(traj_a)
+    segs_b = traj_to_segments(traj_b)
+    raw_count = 0
+    angled_count = 0
+    for (ax, ay), (bx, by) in segs_a:
+        for (cx, cy), (dx, dy) in segs_b:
+            if segments_intersect(ax, ay, bx, by, cx, cy, dx, dy):
+                raw_count += 1
+                angle = segment_angle(ax, ay, bx, by, cx, cy, dx, dy)
+                if angle >= angle_threshold_rad:
+                    angled_count += 1
+    return raw_count, angled_count
+
+
+# ── scoring worker ───────────────────────────────────────────────────────────
+
+# Default angle threshold: 15 degrees. Intersections below this are
+# considered near-parallel (e.g. highway lane changes) and won't count
+# as "angled" intersections.
+ANGLE_THRESHOLD_DEG = 15
+ANGLE_THRESHOLD_RAD = math.radians(ANGLE_THRESHOLD_DEG)
+
+
+def process_scenario(filepath):
+    """Score a single scenario file. Returns tuple for DataFrame construction."""
+    filepath = Path(filepath)
+    try:
+        with open(filepath) as f:
+            data = json.load(f)
+
+        metadata = data.get("metadata", {})
+        sdc_idx = metadata.get("sdc_track_index", -1)
+        objects = data.get("objects", [])
+
+        if sdc_idx < 0 or sdc_idx >= len(objects):
+            return (str(filepath.name), 0, 0, len(objects), sdc_idx, None)
+
+        sdc_traj = extract_trajectory(objects[sdc_idx])
+
+        total_raw = 0
+        total_angled = 0
+        for i, obj in enumerate(objects):
+            if i == sdc_idx:
+                continue
+            raw, angled = score_intersections(sdc_traj, extract_trajectory(obj), ANGLE_THRESHOLD_RAD)
+            total_raw += raw
+            total_angled += angled
+
+        return (str(filepath.name), total_raw, total_angled, len(objects), sdc_idx, None)
+
+    except Exception as e:
+        return (str(filepath.name), -1, 0, 0, -1, str(e))
+
+
+# ── visualization ────────────────────────────────────────────────────────────
+
+ROAD_COLORS = {
+    "lane": "#777777",
+    "road_line": "#AAAAAA",
+    "road_edge": "#999999",
+    "stop_sign": "#FF4444",
+    "crosswalk": "#DDAA33",
+    "speed_bump": "#BB8833",
+}
+DEFAULT_ROAD_COLOR = "#888888"
+
+
+def draw_roads(ax, roads):
+    for road in roads:
+        geometry = road.get("geometry", [])
+        if len(geometry) < 2:
+            continue
+        xs = [p.get("x", 0) for p in geometry]
+        ys = [p.get("y", 0) for p in geometry]
+        road_type = road.get("type", "")
+        color = ROAD_COLORS.get(road_type, DEFAULT_ROAD_COLOR)
+        lw = 0.5 if road_type == "lane" else 0.8
+        ax.plot(xs, ys, color=color, linewidth=lw, alpha=0.6, zorder=1)
+
+
+def visualize_scenario(data, title="", ax=None, crop_radius=60):
+    """Plot road graph + trajectories. SDC green, others blue, intersections red.
+
+    Args:
+        crop_radius: half-width (meters) of the square crop centered on the
+            SDC trajectory centroid. Set to None to autoscale instead.
+    """
+    own_fig = ax is None
+    if own_fig:
+        fig, ax = plt.subplots(1, 1, figsize=(14, 14))
+
+    metadata = data.get("metadata", {})
+    sdc_idx = metadata.get("sdc_track_index", -1)
+    objects = data.get("objects", [])
+    roads = data.get("roads", [])
+
+    # Roads
+    draw_roads(ax, roads)
+
+    # Trajectories
+    sdc_traj = None
+    other_trajs = []
+
+    for i, obj in enumerate(objects):
+        traj = extract_trajectory(obj)
+        segs = traj_to_segments(traj)
+        if not segs:
+            continue
+
+        valid_pts = [p for p in traj if p is not None]
+        mid_pt = valid_pts[len(valid_pts) // 2] if valid_pts else None
+
+        if i == sdc_idx:
+            sdc_traj = traj
+            lines = mc.LineCollection(segs, colors="#66FF66", linewidths=2.5, zorder=4, label="SDC")
+            ax.add_collection(lines)
+            ax.plot(segs[0][0][0], segs[0][0][1], "o", color="#44DD44", markersize=7, zorder=5)
+            ax.plot(segs[-1][1][0], segs[-1][1][1], "s", color="#33BB33", markersize=7, zorder=5)
+            if mid_pt:
+                ax.text(
+                    mid_pt[0],
+                    mid_pt[1],
+                    str(i),
+                    color="#66FF66",
+                    fontsize=8,
+                    fontweight="bold",
+                    ha="center",
+                    va="center",
+                    zorder=8,
+                    clip_on=True,
+                    bbox=dict(boxstyle="round,pad=0.15", facecolor="#000000", edgecolor="none", alpha=0.6),
+                )
+        else:
+            other_trajs.append(traj)
+            lines = mc.LineCollection(segs, colors="#6699FF", linewidths=1.2, alpha=0.7, zorder=3)
+            ax.add_collection(lines)
+            if mid_pt:
+                ax.text(
+                    mid_pt[0],
+                    mid_pt[1],
+                    str(i),
+                    color="white",
+                    fontsize=6,
+                    ha="center",
+                    va="center",
+                    zorder=7,
+                    clip_on=True,
+                    bbox=dict(boxstyle="round,pad=0.12", facecolor="#000000", edgecolor="none", alpha=0.5),
+                )
+
+    # Intersection points
+    intersection_count = 0
+    if sdc_traj is not None:
+        sdc_segs = traj_to_segments(sdc_traj)
+        ix_pts = []
+        for other_traj in other_trajs:
+            other_segs = traj_to_segments(other_traj)
+            for (ax1, ay1), (bx1, by1) in sdc_segs:
+                for (cx1, cy1), (dx1, dy1) in other_segs:
+                    if segments_intersect(ax1, ay1, bx1, by1, cx1, cy1, dx1, dy1):
+                        pt = segment_intersection_point(ax1, ay1, bx1, by1, cx1, cy1, dx1, dy1)
+                        if pt:
+                            ix_pts.append(pt)
+                            intersection_count += 1
+        if ix_pts:
+            ax.scatter(
+                [p[0] for p in ix_pts],
+                [p[1] for p in ix_pts],
+                color="#FF6666",
+                s=30,
+                zorder=6,
+                edgecolors="white",
+                linewidths=0.5,
+                label=f"Intersections ({len(ix_pts)})",
+            )
+
+    ax.set_aspect("equal")
+    ax.set_facecolor("#1a1a1a")
+    if own_fig:
+        ax.figure.set_facecolor("#1a1a1a")
+    ax.axis("off")
+    ax.legend(loc="upper right", fontsize=8, facecolor="#222", edgecolor="#444", labelcolor="white")
+
+    # Crop to a fixed square centered on the SDC trajectory centroid
+    if crop_radius is not None and sdc_traj is not None:
+        sdc_pts = [p for p in sdc_traj if p is not None]
+        if sdc_pts:
+            cx = sum(p[0] for p in sdc_pts) / len(sdc_pts)
+            cy = sum(p[1] for p in sdc_pts) / len(sdc_pts)
+            ax.set_xlim(cx - crop_radius, cx + crop_radius)
+            ax.set_ylim(cy - crop_radius, cy + crop_radius)
+            # Clip every artist to the axes rectangle so nothing
+            # bleeds outside the crop in the saved image.
+            for artist in ax.get_children():
+                artist.set_clip_on(True)
+                artist.set_clip_box(ax.clipbox)
+    else:
+        ax.autoscale_view()
+    return ax
+
+
+# ── main ─────────────────────────────────────────────────────────────────────
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Find, rank, and export the most interactive driving scenarios")
+    parser.add_argument(
+        "--data_folder", type=str, default="data/processed/training", help="Source folder with scenario JSONs"
+    )
+    parser.add_argument(
+        "--output_folder",
+        type=str,
+        default="data/processed/interactive_data_training",
+        help="Where to copy the top-K interactive scenario JSONs",
+    )
+    parser.add_argument(
+        "--dataframe_path",
+        type=str,
+        default="data/meta_info/interactive_ranking.csv",
+        help="Path to save the full ranking dataframe (csv or parquet)",
+    )
+    parser.add_argument("--top_k", type=int, default=500, help="Number of most interactive scenarios to copy")
+    parser.add_argument("--max_files", type=int, default=None, help="Cap on number of source files to process")
+    parser.add_argument("--num_workers", type=int, default=None)
+    parser.add_argument(
+        "--prioritize_intersections",
+        action="store_true",
+        help="Rank scenarios with angled trajectory crossings above all others",
+    )
+    parser.add_argument(
+        "--angle_threshold",
+        type=float,
+        default=5.0,
+        help="Min acute angle (degrees) to count as an angled intersection (default: 15)",
+    )
+
+    # Visualization
+    parser.add_argument("--visualize", action="store_true", help="Generate plots for the top scenarios")
+    parser.add_argument(
+        "--plot_folder", type=str, default="data/meta_info/interactive_plots", help="Folder to store visualization PNGs"
+    )
+    parser.add_argument(
+        "--vis_top_k", type=int, default=None, help="How many top scenarios to visualize (defaults to --top_k)"
+    )
+    parser.add_argument("--dpi", type=int, default=150)
+    parser.add_argument(
+        "--crop_radius",
+        type=float,
+        default=60,
+        help="Half-width (m) of the square crop centered on the SDC centroid. 0 = autoscale.",
+    )
+
+    args = parser.parse_args()
+    num_workers = args.num_workers or cpu_count()
+
+    # Override the module-level angle threshold with CLI arg
+    global ANGLE_THRESHOLD_RAD
+    ANGLE_THRESHOLD_RAD = math.radians(args.angle_threshold)
+
+    # ── 1. Score all scenarios ───────────────────────────────────────────────
+    data_dir = Path(args.data_folder)
+    json_files = sorted(data_dir.glob("*.json"))
+    if args.max_files:
+        json_files = json_files[: args.max_files]
+
+    print(f"Found {len(json_files)} scenario files in {data_dir}")
+    print(f"Using {num_workers} workers")
+    print(f"Angle threshold: {args.angle_threshold}°")
+    if args.prioritize_intersections:
+        print("Prioritizing scenarios with angled intersections\n")
+    else:
+        print()
+
+    with Pool(num_workers) as pool:
+        results = list(
+            tqdm(
+                pool.imap_unordered(process_scenario, [str(f) for f in json_files], chunksize=64),
+                total=len(json_files),
+                desc="Scoring scenarios",
+                unit="file",
+            )
+        )
+
+    # Separate successes / failures (error is last element)
+    successes = [r for r in results if r[5] is None]
+    failures = [r for r in results if r[5] is not None]
+
+    if failures:
+        print(f"\n{len(failures)} files failed:")
+        for entry in failures[:20]:
+            print(f"  {entry[0]}: {entry[5]}")
+
+    # ── 2. Build & save dataframe ────────────────────────────────────────────
+    df = pd.DataFrame(
+        [(name, raw, angled, n_agents, sdc_idx) for name, raw, angled, n_agents, sdc_idx, _ in successes],
+        columns=["filename", "sdc_intersections", "angled_intersections", "num_agents", "sdc_track_index"],
+    )
+
+    # Sort: if prioritize_intersections, put all scenes with angled crossings
+    # first (sorted by raw count within that group), then the rest by raw count.
+    # Otherwise just sort by raw count.
+    if args.prioritize_intersections:
+        df["has_angled"] = (df["angled_intersections"] > 0).astype(int)
+        df = df.sort_values(["has_angled", "sdc_intersections"], ascending=[False, False]).reset_index(drop=True)
+        df = df.drop(columns=["has_angled"])
+    else:
+        df = df.sort_values("sdc_intersections", ascending=False).reset_index(drop=True)
+
+    df_path = Path(args.dataframe_path)
+    df_path.parent.mkdir(parents=True, exist_ok=True)
+
+    if df_path.suffix == ".parquet":
+        df.to_parquet(df_path, index=False)
+    else:
+        df.to_csv(df_path, index=False)
+    print(f"\nSaved ranking dataframe ({len(df)} rows) to {df_path}")
+
+    # Summary stats
+    counts = df["sdc_intersections"]
+    angled = df["angled_intersections"]
+    print(f"  Raw intersections    — max: {counts.max()},  median: {counts.median():.0f},  mean: {counts.mean():.1f}")
+    print(f"  Angled intersections — max: {angled.max()},  median: {angled.median():.0f},  mean: {angled.mean():.1f}")
+    print(f"  Zero-intersection:     {(counts == 0).sum()}")
+    print(f"  Has angled (>0):       {(angled > 0).sum()}")
+
+    # ── 2b. Plot interactivity distribution (WOSAC style) ────────────────
+    plot_dir = Path(args.plot_folder)
+    plot_dir.mkdir(parents=True, exist_ok=True)
+
+    # Style setup — matches plot_wosac_results exactly
+    sns.set("notebook", font_scale=1.05, rc={"figure.figsize": (18, 5)})
+    sns.set_style("ticks", rc={"figure.facecolor": "none", "axes.facecolor": "none"})
+    warnings.filterwarnings("ignore")
+    plt.set_loglevel("WARNING")
+    mpl.rcParams["lines.markersize"] = 8
+
+    palette = sns.color_palette("Set2", n_colors=3)
+
+    fig, axes = plt.subplots(1, 3, figsize=(18, 5))
+
+    # (0) Histogram — raw intersection count
+    ax = axes[0]
+    ax.hist(counts, bins=60, alpha=0.8, color=palette[0], edgecolor="black")
+    ax.axvline(
+        counts.median(),
+        color="red",
+        linestyle="--",
+        linewidth=2,
+        label=f"Median: {counts.median():.0f}",
+    )
+    mean_val = counts.mean()
+    std_val = counts.std()
+    ax.text(
+        0.95,
+        0.95,
+        f"μ = {mean_val:.1f}\nσ = {std_val:.1f}",
+        transform=ax.transAxes,
+        verticalalignment="top",
+        horizontalalignment="right",
+        bbox=dict(boxstyle="round", facecolor="white", alpha=0.8),
+    )
+    ax.set_xlabel("Raw intersection count")
+    ax.set_ylabel("Number of scenarios")
+    ax.set_title("Raw intersections (all)")
+    ax.legend(fontsize=8)
+    ax.grid(axis="y", alpha=0.3, linestyle="--")
+
+    # (1) Histogram — angled intersection count
+    ax = axes[1]
+    angled_nonzero = angled[angled > 0]
+    if len(angled_nonzero) > 0:
+        ax.hist(angled_nonzero, bins=60, alpha=0.8, color=palette[1], edgecolor="black")
+        mean_a = angled_nonzero.mean()
+        std_a = angled_nonzero.std()
+        ax.axvline(
+            mean_a,
+            color="red",
+            linestyle="--",
+            linewidth=2,
+            label=f"Mean: {mean_a:.1f}",
+        )
+        ax.text(
+            0.95,
+            0.95,
+            f"μ = {mean_a:.1f}\nσ = {std_a:.1f}",
+            transform=ax.transAxes,
+            verticalalignment="top",
+            horizontalalignment="right",
+            bbox=dict(boxstyle="round", facecolor="white", alpha=0.8),
+        )
+        ax.legend(fontsize=8)
+    ax.set_xlabel(f"Angled intersection count (≥{args.angle_threshold}°)")
+    ax.set_ylabel("Number of scenarios")
+    ax.set_title(f"Angled intersections — non-zero ({len(angled_nonzero)}/{len(angled)})")
+    ax.grid(axis="y", alpha=0.3, linestyle="--")
+
+    # (2) Histogram — selected top-K scenarios
+    ax = axes[2]
+    top_counts = df.head(args.top_k)["sdc_intersections"]
+    ax.hist(top_counts, bins=60, alpha=0.8, color=palette[2], edgecolor="black")
+    mean_top = top_counts.mean()
+    std_top = top_counts.std()
+    ax.axvline(
+        mean_top,
+        color="red",
+        linestyle="--",
+        linewidth=2,
+        label=f"Mean: {mean_top:.1f}",
+    )
+    ax.text(
+        0.95,
+        0.95,
+        f"μ = {mean_top:.1f}\nσ = {std_top:.1f}\nmin = {top_counts.min()}",
+        transform=ax.transAxes,
+        verticalalignment="top",
+        horizontalalignment="right",
+        bbox=dict(boxstyle="round", facecolor="white", alpha=0.8),
+    )
+    ax.set_xlabel("Raw intersection count")
+    ax.set_ylabel("Number of scenarios")
+    ax.set_title(f"Selected top-{args.top_k} scenarios")
+    ax.legend(fontsize=8)
+    ax.grid(axis="y", alpha=0.3, linestyle="--")
+
+    # Despine all panels (WOSAC style)
+    for ax in axes:
+        sns.despine(ax=ax)
+
+    plt.tight_layout()
+    dist_path = plot_dir / "interactivity_distribution.png"
+    fig.savefig(dist_path, dpi=args.dpi, bbox_inches="tight", facecolor="white")
+    plt.close(fig)
+    print(f"Saved distribution plot to {dist_path}")
+
+    # ── 3. Copy top-K to output folder ───────────────────────────────────────
+    top_df = df.head(args.top_k)
+    out_dir = Path(args.output_folder)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    copied = 0
+    for filename in tqdm(top_df["filename"], desc="Copying top-K files", unit="file"):
+        src = data_dir / filename
+        dst = out_dir / filename
+        if src.exists():
+            shutil.copy2(src, dst)
+            copied += 1
+
+    print(f"\nCopied {copied} interactive scenarios to {out_dir}")
+
+    # Print top 10
+    print("\nTop 10 most interactive scenarios:")
+    for _, row in top_df.head(10).iterrows():
+        print(
+            f"  {row['filename']:>55s}  raw={row['sdc_intersections']:4d}  "
+            f"angled={row['angled_intersections']:4d}  agents={row['num_agents']}"
+        )
+
+    # ── 4. Visualize (optional) ──────────────────────────────────────────────
+    if args.visualize:
+        vis_k = args.vis_top_k or args.top_k
+        vis_df = df.head(vis_k)
+
+        plot_dir = Path(args.plot_folder)
+        plot_dir.mkdir(parents=True, exist_ok=True)
+
+        print(f"\nRendering {len(vis_df)} scenario plots to {plot_dir}")
+        for idx, row in tqdm(vis_df.iterrows(), total=len(vis_df), desc="Plotting", unit="plot"):
+            fpath = data_dir / row["filename"]
+            if not fpath.exists():
+                continue
+
+            with open(fpath) as f:
+                data = json.load(f)
+
+            fig, ax = plt.subplots(1, 1, figsize=(8, 8))
+            fig.set_facecolor("#1a1a1a")
+            ax.set_position([0, 0, 1, 1])  # axes fills entire figure
+            crop = args.crop_radius if args.crop_radius > 0 else None
+            visualize_scenario(data, title=row["filename"], ax=ax, crop_radius=crop)
+
+            out_name = fpath.stem + ".png"
+            fig.savefig(
+                plot_dir / out_name,
+                dpi=args.dpi,
+                facecolor=fig.get_facecolor(),
+            )
+            plt.close(fig)
+
+        print(f"Saved {len(vis_df)} plots to {plot_dir}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/train_bc_policy.py b/examples/train_bc_policy.py
new file mode 100644
index 0000000000..ca8c9c8db2
--- /dev/null
+++ b/examples/train_bc_policy.py
@@ -0,0 +1,554 @@
+"""
+BC Policy training script with wandb sweep support.
+
+Usage:
+    python examples/train_bc_policy.py train                        # single run, default config
+    python examples/train_bc_policy.py train --dynamics classic     # single run, classic dynamics
+    python examples/train_bc_policy.py train --map-dir path/to/maps --num-maps 25000
+    python examples/train_bc_policy.py sweep                        # launch sweep + agent
+    python examples/train_bc_policy.py --dynamics classic
+"""
+
+import argparse
+import os
+import wandb
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.optim import Adam
+from torch.utils.data import DataLoader, TensorDataset
+from torch.distributions.categorical import Categorical
+import numpy as np
+import matplotlib.pyplot as plt
+
+from pufferlib.pufferl import load_config, load_env
+from pufferlib.ocean.drive import binding
+import pufferlib
+import pufferlib.models
+
+CHECKPOINT_PATH = "models"
+os.makedirs(CHECKPOINT_PATH, exist_ok=True)
+
+# ---------------------------------------------------------------------------
+# Sweep config
+# ---------------------------------------------------------------------------
+SWEEP_CONFIG = {
+    "method": "bayes",
+    "metric": {"name": "best_avg_loss", "goal": "minimize"},
+    "parameters": {
+        "learning_rate": {"distribution": "log_uniform_values", "min": 3e-5, "max": 1e-3},
+        "input_size": {"values": [64, 128]},
+        "hidden_size": {"values": [512, 1024]},
+        "batch_size": {"values": [2048]},
+        "resample_every_n_epochs": {"values": [1, 5, 10]},
+        "num_maps": {"values": [50000]},
+    },
+}
+
+TRAIN_DEFAULTS = {
+    "learning_rate": 1e-4,
+    "input_size": 128,
+    "hidden_size": 512,
+    "batch_size": 2048,
+    "resample_every_n_epochs": 1,  # Resample after k full passes through the dataset
+    "epochs": 10,
+    "num_maps": 50000,
+    "eval_frequency": 10,  # Validation dataset
+}
+
+
+class BCPolicy(nn.Module):
+    """BC policy supporting both joint (single head) and independent (multi-head) action spaces."""
+
+    def __init__(
+        self,
+        obs_dim,
+        input_size,
+        max_partner_objects,
+        partner_features,
+        max_road_objects,
+        road_features,
+        ego_dim,
+        hidden_size,
+        output_sizes,
+    ):
+        super().__init__()
+
+        self.num_heads = len(output_sizes)
+        self.hidden_size = hidden_size
+        self.obs_dim = obs_dim
+
+        self.max_partner_objects = max_partner_objects
+        self.partner_features = partner_features
+        self.max_road_objects = max_road_objects
+        self.road_features = road_features
+        self.road_features_after_onehot = road_features + 6
+        self.ego_dim = ego_dim
+
+        self.ego_encoder = nn.Sequential(
+            pufferlib.pytorch.layer_init(nn.Linear(self.ego_dim, input_size)),
+            nn.ReLU(),
+            nn.LayerNorm(input_size),
+            pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)),
+        )
+        self.road_encoder = nn.Sequential(
+            pufferlib.pytorch.layer_init(nn.Linear(self.road_features_after_onehot, input_size)),
+            nn.ReLU(),
+            nn.LayerNorm(input_size),
+            pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)),
+        )
+        self.partner_encoder = nn.Sequential(
+            pufferlib.pytorch.layer_init(nn.Linear(self.partner_features, input_size)),
+            nn.ReLU(),
+            nn.LayerNorm(input_size),
+            pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)),
+        )
+        self.shared_embedding = nn.Sequential(
+            pufferlib.pytorch.layer_init(nn.Linear(3 * input_size, 512)),
+            nn.ReLU(),
+            pufferlib.pytorch.layer_init(nn.Linear(512, hidden_size)),
+        )
+        self.heads = nn.ModuleList([nn.Linear(hidden_size, s) for s in output_sizes])
+
+    def encode_observations(self, observations, state=None):
+        ego_dim = self.ego_dim
+        partner_dim = self.max_partner_objects * self.partner_features
+        ego_obs = observations[:, :ego_dim]
+        partner_obs = observations[:, ego_dim : ego_dim + partner_dim]
+        road_obs = observations[:, ego_dim + partner_dim :]
+
+        partner_objects = partner_obs.view(-1, self.max_partner_objects, self.partner_features)
+        road_objects = road_obs.view(-1, self.max_road_objects, self.road_features)
+        road_continuous = road_objects[:, :, : self.road_features - 1]
+        road_categorical = road_objects[:, :, self.road_features - 1]
+        road_onehot = F.one_hot(road_categorical.long(), num_classes=7)
+        road_objects = torch.cat([road_continuous, road_onehot], dim=2)
+
+        ego_features = self.ego_encoder(ego_obs)
+        partner_features, _ = self.partner_encoder(partner_objects).max(dim=1)
+        road_features, _ = self.road_encoder(road_objects).max(dim=1)
+
+        concat_features = torch.cat([ego_features, road_features, partner_features], dim=1)
+        return F.relu(self.shared_embedding(concat_features))
+
+    def dist(self, obs):
+        hidden = self.encode_observations(obs.float())
+        return [Categorical(logits=head(hidden)) for head in self.heads]
+
+    def forward(self, obs, deterministic=False):
+        dists = self.dist(obs)
+        actions = [d.logits.argmax(dim=-1) if deterministic else d.sample() for d in dists]
+        return actions[0] if self.num_heads == 1 else torch.stack(actions, dim=-1)
+
+    def get_action_dist_logits(self, obs):
+        """Get logits from all heads. Returns list of tensors."""
+        return [d.logits for d in self.dist(obs)]
+
+    def _log_prob(self, obs, expert_actions):
+        dists = self.dist(obs)
+        if self.num_heads == 1:
+            return dists[0].log_prob(expert_actions.squeeze(-1).long()).mean()
+        log_prob = sum(d.log_prob(expert_actions[:, i].long()).mean() for i, d in enumerate(dists))
+        return log_prob / self.num_heads
+
+    def _entropy(self, obs):
+        dists = self.dist(obs)
+        if self.num_heads == 1:
+            return dists[0].entropy().mean().item()
+        return {f"entropy_head_{i}": d.entropy().mean().item() for i, d in enumerate(dists)}
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+def get_output_sizes(dynamics_model):
+    if dynamics_model == "classic":
+        return [binding.NUM_ACCEL_BINS * binding.NUM_STEER_BINS]
+    if dynamics_model == "delta_local":
+        return [binding.NUM_DX_BINS, binding.NUM_DY_BINS, binding.NUM_YAW_BINS]
+    raise ValueError(f"Unknown dynamics model: {dynamics_model}")
+
+
+def build_env_args(dynamics_model, num_maps):
+    args = load_config("puffer_drive")
+    args["vec"]["backend"] = "Serial"
+    args["env"]["num_maps"] = num_maps
+    args["env"]["map_dir"] = "resources/drive/binaries/training_50k"
+    args["env"]["reg_mode"] = "log_prob_direct"
+    args["env"]["dynamics_model"] = dynamics_model
+    args["base"]["rnn_name"] = "none"
+    args["env"]["fix_lambdas"] = True
+    args["env"]["fix_rewards"] = True
+    args["env"]["lambda_value"] = 0.0
+    return args
+
+
+_cumulative_unique_hashes: set = set()
+
+
+def load_data(driver_env):
+    driver_env.resample_maps()
+    total_samples, unique_samples = driver_env._prepare_human_data()
+
+    obs_np = driver_env.expert_observations_full.numpy()
+    act_np = driver_env.expert_actions_discrete.numpy()
+    batch_hashes = {driver_env._hash_pair(obs_np[i], act_np[i]) for i in range(len(obs_np))}
+    _cumulative_unique_hashes.update(batch_hashes)
+
+    print(
+        f"Resampled: {total_samples} samples ({unique_samples} unique, {len(_cumulative_unique_hashes)} cumulative unique)"
+    )
+    wandb.log(
+        {
+            "data/total_samples": total_samples,
+            "data/unique_samples": unique_samples,
+            "data/cumulative_unique_samples": len(_cumulative_unique_hashes),
+        }
+    )
+    obs = driver_env.expert_observations_full.float()
+    actions = driver_env.expert_actions_discrete.long()
+    return TensorDataset(obs, actions)
+
+
+def compute_accuracy(policy, batch_obs, batch_actions):
+    with torch.no_grad():
+        pred = policy(batch_obs, deterministic=True)
+        if policy.num_heads == 1:
+            return (batch_actions.squeeze(-1) == pred).float().mean().item()
+        return (batch_actions == pred).float().mean(dim=0).mean().item()
+
+
+def save_action_distribution_plot(policy, dataset, dynamics_model, num_maps, run_id, device):
+    for batch_obs, batch_actions in DataLoader(dataset, batch_size=len(dataset)):
+        all_obs = batch_obs
+        all_actions = batch_actions.numpy()
+
+    if dynamics_model == "classic":
+        NUM_STEER = binding.NUM_STEER_BINS
+        NUM_ACCEL = binding.NUM_ACCEL_BINS
+        accel_step = 8.0 / (NUM_ACCEL - 1)
+        steer_step = 2.0 / (NUM_STEER - 1)
+
+        accel_idx = all_actions.flatten().astype(int) // NUM_STEER
+        steer_idx = all_actions.flatten().astype(int) % NUM_STEER
+        accel_vals = -4.0 + accel_idx * accel_step
+        steer_vals = -1.0 + steer_idx * steer_step
+
+        policy.eval()
+        with torch.no_grad():
+            pred = policy(all_obs.to(device), deterministic=True).cpu().numpy().flatten()
+        pred_accel_idx = pred.astype(int) // NUM_STEER
+        pred_steer_idx = pred.astype(int) % NUM_STEER
+        pred_accel_vals = -4.0 + pred_accel_idx * accel_step
+        pred_steer_vals = -1.0 + pred_steer_idx * steer_step
+
+        fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+        fig.suptitle(
+            f"BC Training Data vs Learned Policy — {len(all_actions)} samples, {dynamics_model}, {num_maps} maps",
+            fontsize=14,
+        )
+        for ax, vals, title, color in [
+            (axes[0, 0], steer_vals, f"Expert Steering (non-zero: {(steer_idx != NUM_STEER // 2).sum()})", "steelblue"),
+            (
+                axes[0, 1],
+                pred_steer_vals,
+                f"Learned Steering (non-zero: {(pred_steer_idx != NUM_STEER // 2).sum()})",
+                "orange",
+            ),
+            (axes[1, 0], accel_vals, "Expert Acceleration", "steelblue"),
+            (axes[1, 1], pred_accel_vals, "Learned Acceleration", "orange"),
+        ]:
+            bins, rng = (NUM_STEER, (-1.0, 1.0)) if "Steer" in title else (NUM_ACCEL, (-4.0, 4.0))
+            xlabel = "Steering (rad)" if "Steer" in title else "Acceleration (m/s²)"
+            ax.hist(vals, bins=bins, range=rng, edgecolor="black", alpha=0.7, color=color)
+            ax.set_xlabel(xlabel)
+            ax.set_ylabel("Count")
+            ax.set_title(title)
+            ax.axvline(x=0, color="r", linestyle="--", alpha=0.5)
+
+    elif dynamics_model == "delta_local":
+        NUM_DX = binding.NUM_DX_BINS
+        NUM_DY = binding.NUM_DY_BINS
+        NUM_YAW = binding.NUM_YAW_BINS
+        MAX_DX = 2.0
+        MAX_DY = 2.0
+        MAX_DYAW = np.pi / 4.0
+
+        dx_step = 2 * MAX_DX / (NUM_DX - 1)
+        dy_step = 2 * MAX_DY / (NUM_DY - 1)
+        yaw_step = 2 * MAX_DYAW / (NUM_YAW - 1)
+
+        expert_dx_vals = -MAX_DX + all_actions[:, 0].astype(int) * dx_step
+        expert_dy_vals = -MAX_DY + all_actions[:, 1].astype(int) * dy_step
+        expert_yaw_vals = -MAX_DYAW + all_actions[:, 2].astype(int) * yaw_step
+
+        policy.eval()
+        with torch.no_grad():
+            pred = policy(all_obs.to(device), deterministic=True).cpu().numpy()
+        pred_dx_vals = -MAX_DX + pred[:, 0].astype(int) * dx_step
+        pred_dy_vals = -MAX_DY + pred[:, 1].astype(int) * dy_step
+        pred_yaw_vals = -MAX_DYAW + pred[:, 2].astype(int) * yaw_step
+
+        fig, axes = plt.subplots(3, 2, figsize=(14, 14))
+        fig.suptitle(
+            f"BC Training Data vs Learned Policy — {len(all_actions)} samples, {dynamics_model}, {num_maps} maps",
+            fontsize=14,
+        )
+        plot_configs = [
+            (axes[0, 0], expert_dx_vals, "Expert ΔX", "steelblue", NUM_DX, (-MAX_DX, MAX_DX), "ΔX (m)"),
+            (axes[0, 1], pred_dx_vals, "Learned ΔX", "orange", NUM_DX, (-MAX_DX, MAX_DX), "ΔX (m)"),
+            (axes[1, 0], expert_dy_vals, "Expert ΔY", "steelblue", NUM_DY, (-MAX_DY, MAX_DY), "ΔY (m)"),
+            (axes[1, 1], pred_dy_vals, "Learned ΔY", "orange", NUM_DY, (-MAX_DY, MAX_DY), "ΔY (m)"),
+            (axes[2, 0], expert_yaw_vals, "Expert ΔYaw", "steelblue", NUM_YAW, (-MAX_DYAW, MAX_DYAW), "ΔYaw (rad)"),
+            (axes[2, 1], pred_yaw_vals, "Learned ΔYaw", "orange", NUM_YAW, (-MAX_DYAW, MAX_DYAW), "ΔYaw (rad)"),
+        ]
+        for ax, vals, title, color, bins, rng, xlabel in plot_configs:
+            ax.hist(vals, bins=bins, range=rng, edgecolor="black", alpha=0.7, color=color)
+            ax.set_xlabel(xlabel)
+            ax.set_ylabel("Count")
+            ax.set_title(title)
+            ax.axvline(x=0, color="r", linestyle="--", alpha=0.5)
+
+    else:
+        return
+
+    plt.tight_layout()
+    path = f"bc_action_distribution_{dynamics_model}_{num_maps}_{run_id}.png"
+    plt.savefig(path, dpi=150)
+    plt.close(fig)
+    print(f"Saved action distribution plot to {path}")
+    wandb.log({"action_distribution": wandb.Image(path)})
+
+
+def evaluate(policy, dataloader, device):
+    policy.eval()
+    losses, accuracies = [], []
+    entropy_accum = {}
+
+    with torch.no_grad():
+        for batch_obs, batch_actions in dataloader:
+            batch_obs = batch_obs.to(device)
+            batch_actions = batch_actions.to(device)
+
+            loss = -policy._log_prob(batch_obs, batch_actions.float())
+            accuracy = compute_accuracy(policy, batch_obs, batch_actions)
+            entropy = policy._entropy(batch_obs)
+
+            losses.append(loss.item())
+            accuracies.append(accuracy)
+            if isinstance(entropy, dict):
+                for k, v in entropy.items():
+                    entropy_accum.setdefault(k, []).append(v)
+            else:
+                entropy_accum.setdefault("entropy", []).append(entropy)
+
+    policy.train()
+    metrics = {
+        "loss": np.mean(losses),
+        "accuracy": np.mean(accuracies),
+        **{k: np.mean(v) for k, v in entropy_accum.items()},
+    }
+    return metrics
+
+
+# ---------------------------------------------------------------------------
+# Core training loop (called by both single-run and sweep agent)
+# ---------------------------------------------------------------------------
+def train(dynamics_model: str, map_dir: str = None, num_maps_override: int = None):
+    output_sizes = get_output_sizes(dynamics_model)
+
+    run = wandb.init(
+        project="bc_anchor_policy",
+        tags=["bc_policy", dynamics_model],
+        config={**TRAIN_DEFAULTS, "dynamics_model": dynamics_model, "output_sizes": output_sizes},
+    )
+
+    # Apply CLI overrides to wandb config
+    if num_maps_override is not None:
+        wandb.config.update({"num_maps": num_maps_override}, allow_val_change=True)
+    if map_dir is not None:
+        wandb.config.update({"map_dir": map_dir}, allow_val_change=True)
+
+    config = wandb.config
+
+    lr = config.learning_rate
+    hidden_size = config.hidden_size
+    batch_size = config.batch_size
+    resample_every = config.resample_every_n_epochs
+    epochs = config.epochs
+    num_maps = config.num_maps
+    eval_frequency = config.eval_frequency
+
+    print(
+        f"dynamics={dynamics_model}  lr={lr}  hidden={hidden_size}  num_maps={num_maps}"
+        f"batch={batch_size}  resample_every={resample_every}"
+    )
+
+    # Train env
+    args = build_env_args(dynamics_model, num_maps=num_maps)
+    if hasattr(config, "map_dir") and config.map_dir is not None:
+        args["env"]["map_dir"] = config.map_dir
+
+    effective_map_dir = args["env"]["map_dir"]
+    run.name = f"{dynamics_model}_{os.path.basename(effective_map_dir)}_{num_maps}maps"
+
+    env = load_env("puffer_drive", args)
+    driver_env = env.driver_env
+
+    # Validation env: Same as train but uses different set of maps
+    val_args = build_env_args(dynamics_model, num_maps=10000)
+    val_args["env"]["map_dir"] = "resources/drive/binaries/validation"
+    val_env = load_env("puffer_drive", val_args)
+    val_driver_env = val_env.driver_env
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Device: {device}")
+
+    policy = BCPolicy(
+        obs_dim=driver_env.num_obs,
+        input_size=config.input_size,
+        max_partner_objects=driver_env.max_partner_objects,
+        partner_features=driver_env.partner_features,
+        max_road_objects=driver_env.max_road_objects,
+        road_features=driver_env.road_features,
+        ego_dim=driver_env.ego_features,
+        hidden_size=config.hidden_size,
+        output_sizes=output_sizes,
+    ).to(device)
+
+    param_count = sum(p.numel() for p in policy.parameters())
+    print(f"Parameters: {param_count:,}")
+    wandb.log({"model/param_count": param_count})
+
+    optimizer = Adam(policy.parameters(), lr=lr)
+    # Train
+    dataset = load_data(driver_env)
+    minibatches = len(dataset) // batch_size
+    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+    data_iter = iter(dataloader)
+
+    # Validation
+    val_dataset = load_data(val_driver_env)
+    val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
+
+    best_avg_loss = float("inf")
+    global_step = 0
+
+    for epoch in range(epochs):
+        if epoch > 0 and epoch % resample_every == 0:
+            dataset = load_data(driver_env)
+            dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+            data_iter = iter(dataloader)
+
+        epoch_losses = []
+        for _ in range(minibatches):
+            try:
+                batch_obs, batch_actions = next(data_iter)
+            except StopIteration:
+                data_iter = iter(dataloader)
+                batch_obs, batch_actions = next(data_iter)
+
+            batch_obs = batch_obs.to(device)
+            batch_actions = batch_actions.to(device)
+
+            loss = -policy._log_prob(batch_obs, batch_actions.float())
+            entropy = policy._entropy(batch_obs)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            accuracy = compute_accuracy(policy, batch_obs, batch_actions)
+            epoch_losses.append(loss.item())
+
+            # Log statistics
+            wandb.log(
+                {
+                    f"train/{k}": v
+                    for k, v in {
+                        "loss": loss.item(),
+                        "accuracy": accuracy,
+                        "epoch": epoch,
+                        "global_step": global_step,
+                        **(entropy if isinstance(entropy, dict) else {"entropy": entropy}),
+                    }.items()
+                }
+            )
+
+            global_step += 1
+
+        avg_loss = np.mean(epoch_losses)
+        if avg_loss < best_avg_loss:
+            best_avg_loss = avg_loss
+
+        wandb.log({"train/avg_loss": avg_loss, "train/best_avg_loss": best_avg_loss})
+
+        if epoch % eval_frequency == 0:
+            val_metrics = evaluate(policy, val_dataloader, device)
+            wandb.log({"val/" + k: v for k, v in val_metrics.items()})
+            print(f"  val: loss={val_metrics['loss']:.4f}  acc={val_metrics['accuracy']:.4f}")
+
+        print(f"Epoch {epoch + 1}/{epochs}: loss={avg_loss:.4f}  best={best_avg_loss:.4f}")
+
+        if avg_loss < 0.001:
+            print(f"Early stopping at epoch {epoch + 1}")
+            break
+
+    save_path = f"{CHECKPOINT_PATH}/bc_{dynamics_model}_{num_maps}maps_{run.id}.pt"
+    torch.save(policy.state_dict(), save_path)
+    wandb.save(save_path, base_path=".")
+    print(f"Saved checkpoint: {save_path}")
+    wandb.summary["best_avg_loss"] = best_avg_loss
+
+    save_action_distribution_plot(
+        policy,
+        dataset,
+        dynamics_model,
+        num_maps=num_maps,
+        run_id=run.id,
+        device=device,
+    )
+
+    env.close()
+    val_env.close()
+    wandb.finish()
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+def parse_args():
+    parser = argparse.ArgumentParser(description="BC policy training")
+    sub = parser.add_subparsers(dest="mode", required=True)
+
+    for name, help_text in [
+        ("train", "Single training run"),
+        ("sweep", "Create a new sweep and attach an agent"),
+    ]:
+        p = sub.add_parser(name, help=help_text)
+        p.add_argument("--dynamics", choices=["classic", "delta_local"], default="delta_local")
+        if name == "train":
+            p.add_argument("--map-dir", type=str, default=None, help="Override map_dir")
+            p.add_argument("--num-maps", type=int, default=None, help="Override num_maps")
+        if name == "sweep":
+            p.add_argument("--count", type=int, default=50, help="Number of sweep runs")
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    import sys
+
+    args = parse_args()
+    sys.argv = sys.argv[:1]  # strip before load_config sees them
+
+    if args.mode == "train":
+        train(
+            dynamics_model=args.dynamics,
+            map_dir=args.map_dir,
+            num_maps_override=args.num_maps,
+        )
+
+    elif args.mode == "sweep":
+        sweep_id = wandb.sweep(SWEEP_CONFIG, project="bc_anchor_policy")
+        wandb.agent(sweep_id, function=lambda: train(args.dynamics), count=args.count)
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diff --git a/pufferlib/config/ocean/drive.ini b/pufferlib/config/ocean/drive.ini
index e7224203d2..243c943b0f 100644
--- a/pufferlib/config/ocean/drive.ini
+++ b/pufferlib/config/ocean/drive.ini
@@ -25,20 +25,30 @@ hidden_size = 256
 num_agents = 1024
 ; Options: discrete, continuous
 action_type = discrete
-; Options: classic, jerk
-dynamics_model = classic
-reward_vehicle_collision = -0.5
-reward_offroad_collision = -0.5
-dt = 0.1
+; Options: classic, jerk, delta_local
+dynamics_model = delta_local
+; Reward conditioning
+; If True, set the rewards to the values below, if False, we sample U[0, value]
+fix_rewards = True
+fix_lambdas = True
+lambda_value = 0.0
+reward_vehicle_collision = -1.0
+reward_offroad_collision = -1.0
 reward_goal = 1.0
-reward_goal_post_respawn = 0.25
+; Sensor noise
+obs_partner_noise_speed = 0.0
+obs_partner_noise_pos = 0.0
+; Discretization
+dt = 0.1
+; Reset vecenvs asynchronously (leave True for training; False is useful for eval)
+async_resets = True
 ; Meters around goal to be considered "reached"
-goal_radius = 2.0
+goal_radius = 2.5
 ; Max target speed in m/s for the agent to maintain towards the goal
 goal_speed = 100.0
-; What to do when the goal is reached. Options: 0:"respawn", 1:"generate_new_goals", 2:"stop"
+; What to do when the goal is reached. Options: 0:"remove", 1:"generate_new_goals", 2:"stop"
 goal_behavior = 0
-; Determines the target distance to the new goal in the case of goal_behavior = generate_new_goals.
+; Determines the target distance to the new goal ind the case of goal_behavior = generate_new_goals.
 ; Large numbers will select a goal point further away from the agent's current position.
 goal_target_distance = 30.0
 ; Options: 0 - Ignore, 1 - Stop, 2 - Remove
@@ -48,9 +58,9 @@ offroad_behavior = 0
 ; Number of steps before
 episode_length = 91
 resample_frequency = 910
-termination_mode = 1 # 0 - terminate at episode_length, 1 - terminate after all agents have been reset
-map_dir = "resources/drive/binaries/training"
-num_maps = 10000
+termination_mode = 1 # 0 - terminate at episode_length, 1 - terminate after all agents are done
+map_dir = "resources/drive/binaries/training_50k"
+num_maps = 50000
 ; Determines which step of the trajectory to initialize the agents at upon reset
 init_steps = 0
 ; Options: "control_vehicles", "control_agents", "control_wosac", "control_sdc_only", "control_mixed_play"
@@ -63,12 +73,13 @@ max_controlled_agents = 32
 ; 0:"window" = pop-up raylib window (original)
 ; 1:"headless" = off-screen; frames piped to ffmpeg (recommended for training)
 render_mode = 1
+; Options for regularization. Options: "kl_anchor", "log_likelihood", None
+reg_mode = "kl_anchor"
+anchor_cpt_path = "models/bc_delta_local_100maps_8sx0xor5.pt"
 
 [train]
 seed=42
-total_timesteps = 2_000_000_000
-; learning_rate = 0.02
-; gamma = 0.985
+total_timesteps = 10_000_000_000
 anneal_lr = True
 anneal_entropy = False
 ; Needs to be: num_agents * num_workers * rollout_horizon
@@ -84,8 +95,8 @@ adam_eps = 1e-8
 clip_coef = 0.2
 ent_coef = 0.005
 gae_lambda = 0.95
-gamma = 0.98
-learning_rate = 0.003
+gamma = 0.999 #0.981
+learning_rate = 0.004261385334377629
 max_grad_norm = 1
 prio_alpha = 0.8499999999999999
 prio_beta0 = 0.8499999999999999
@@ -94,30 +105,36 @@ vf_clip_coef = 0.1999999999999999
 vf_coef = 2
 vtrace_c_clip = 1
 vtrace_rho_clip = 1
-checkpoint_interval = 1000
+checkpoint_interval = 500
+optimizer = adam
+
+[sweep]
+method = Protein
+metric = eval/hr_mean_did_collide
+goal = minimize
+downsample = 10
 
 [eval]
-; Eval frequency in epochs
-eval_interval = 500
+eval_interval = 1000
 ; Path to dataset used for evaluation
-map_dir = "resources/drive/binaries/training"
+map_dir = "resources/drive/binaries/validation"
 ; Number of agents to evaluate
-num_eval_agents = 64
+num_eval_agents = 512
 ; If True, enable self-play evaluation (pair policy-controlled agent with a copy of itself)
 self_play_eval = True
 ; If True, enable human replay evaluation (pair policy-controlled agent with human replays)
-human_replay_eval = False
+human_replay_eval = True
 ; Which env to render during eval. Options: "first" (by index), "worst_collision", "random"
-render_select_mode = "first"
+render_select_mode = "random"
 ; If True, render random scenarios. Note: Doing this frequency will slow down the training.
-render_human_replay_eval = False
+render_human_replay_eval = True
 render_self_play_eval = True
 ; Number of scenarios to process per batch
-wosac_batch_size = 32
+wosac_batch_size = 64
 ; Target number of unique scenarios perform evaluation in
 wosac_target_scenarios = 64
 ; Total pool of scenarios to sample from
-wosac_scenario_pool_size = 1000
+wosac_scenario_pool_size = 10000
 ; Max batches, used as a timeout to prevent an infinite loop
 wosac_max_batches = 100
 backend = PufferEnv
@@ -132,16 +149,19 @@ wosac_init_steps = 10
 wosac_control_mode = "control_wosac"
 ; Create everything in valid at init the scene
 wosac_init_mode = "create_all_valid"
-; Stop when reaching the goal
+; Stop and terminate when reaching the goal
 wosac_goal_behavior = 2
+; Whether to include post-done transitions in the metric calculations
+wosac_filter_out_post_done = False
 ; Can shrink goal radius for WOSAC evaluation
-wosac_goal_radius = 2.0
+wosac_goal_radius = 2.5
 wosac_sanity_check = False
 ; Only return aggregate results across all scenes
 wosac_aggregate_results = True
 ; Evaluation mode: "policy", "ground_truth"
 wosac_eval_mode = "policy"
 
+; General sweep settings:
 [sweep.train.learning_rate]
 distribution = log_normal
 min = 0.001
@@ -152,29 +172,42 @@ scale = auto
 [sweep.train.ent_coef]
 distribution = log_normal
 min = 0.001
-mean = 0.005
+mean = 0.0069
 max = 0.03
 scale = auto
 
-[sweep.train.gamma]
+[sweep.env.lambda_value]
 distribution = log_normal
-min = 0.97
-mean = 0.98
-max = 0.999
+min = 0.0001
+mean = 0.015
+max = 0.15
 scale = auto
 
-[sweep.train.gae_lambda]
+[sweep.env.obs_partner_noise_pos]
 distribution = log_normal
-min = 0.95
-mean = 0.98
-max = 0.999
+min = 0.000001
+mean = 0.01
+max = 0.05
 scale = auto
 
-[controlled_exp.train.goal_speed]
-values = [10, 20, 30, 3]
+[sweep.env.obs_partner_noise_speed]
+distribution = log_normal
+min = 0.00001
+mean = 0.005
+max = 0.01
+scale = auto
 
-[controlled_exp.train.ent_coef]
-values = [0.001, 0.005, 0.01]
+[controlled_exp.env.lambda_value]
+values = [0.15]
 
 [controlled_exp.train.seed]
-values = [42, 55, 1]
+values = [42, 1]
+
+[controlled_exp.env.num_maps]
+values = [50000]
+
+; [controlled_exp.env.dynamics_model]
+; values = ["classic"]
+
+; [controlled_exp.env.control_mode]
+; values = ["control_agents"]
diff --git a/pufferlib/ocean/benchmark/evaluator.py b/pufferlib/ocean/benchmark/evaluator.py
index e1b61a731a..6cdd02aeba 100644
--- a/pufferlib/ocean/benchmark/evaluator.py
+++ b/pufferlib/ocean/benchmark/evaluator.py
@@ -6,6 +6,8 @@
 import pandas as pd
 from typing import Dict
 import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
 from tqdm import tqdm
 import configparser
 import os
@@ -38,12 +40,14 @@ def __init__(self, config: Dict):
         self.init_steps = config.get("eval", {}).get("wosac_init_steps", 0)
         self.sim_steps = self.num_steps - self.init_steps
         self.num_rollouts = config.get("eval", {}).get("wosac_num_rollouts", 32)
+        self.filter_out_post_done = config.get("eval", {}).get("wosac_filter_out_post_done", True)
         self.device = config.get("train", {}).get("device", "cuda")
         self.eval_mode = config.get("eval", {}).get("wosac_eval_mode", "policy")
 
         wosac_metrics_path = os.path.join(os.path.dirname(__file__), "wosac.ini")
         self.metrics_config = configparser.ConfigParser()
         self.metrics_config.read(wosac_metrics_path)
+        self.mode = "rl"
 
     def evaluate(self, args, vecenv, policy=None, drop_scene_duplicates=True):
         """Run full WOSAC evaluation with batched iteration over target scenarios.
@@ -84,6 +88,7 @@ def evaluate(self, args, vecenv, policy=None, drop_scene_duplicates=True):
                             gt_trajectories[key], args["eval"]["wosac_num_rollouts"], axis=1
                         )
                     simulated_trajectories["id"] = simulated_trajectories["id"][..., np.newaxis]
+                    simulated_trajectories["dones"] = np.zeros_like(simulated_trajectories["x"])
                 else:
                     raise ValueError(f"Policy is None or unknown evaluation mode: {self.eval_mode}")
 
@@ -165,8 +170,15 @@ def collect_ground_truth_trajectories(self, puffer_env):
         """
         return puffer_env.get_ground_truth_trajectories()
 
-    def collect_simulated_trajectories(self, args, puffer_env, policy):
+    def collect_simulated_trajectories(self, args, puffer_env, policy=None, actions=None):
         """Roll out policy in env and collect trajectories.
+        Args:
+            args: configuration dictionary
+            puffer_env: PufferDrive environment
+            policy: policy to evaluate (if None, actions must be provided)
+            actions: actions to step the agent (if policy is None). Currently only works with discrete actions and the
+                classic dynamics model. Shape: [time, num_agents, 1]
+
         Returns:
             trajectories: dict with keys 'x', 'y', 'z', 'heading' each of shape
                 (num_agents, num_rollouts, num_steps)
@@ -182,13 +194,17 @@ def collect_simulated_trajectories(self, args, puffer_env, policy):
             "z": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32),
             "heading": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32),
             "id": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.int32),
+            "dones": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.bool_),
         }
 
-        for rollout_idx in range(self.num_rollouts):
+        for rollout_idx in tqdm(range(self.num_rollouts), desc="Collecting rollouts", colour="blue"):
             print(f"\rCollecting rollout {rollout_idx + 1}/{self.num_rollouts}...", end="", flush=True)
+
             obs, info = puffer_env.reset()
+            truncations = np.zeros((num_agents,), dtype=bool)
             state = {}
-            if args["train"]["use_rnn"]:
+
+            if args["train"]["use_rnn"] and policy is not None:
                 state = dict(
                     lstm_h=torch.zeros(num_agents, policy.hidden_size, device=device),
                     lstm_c=torch.zeros(num_agents, policy.hidden_size, device=device),
@@ -202,18 +218,39 @@ def collect_simulated_trajectories(self, args, puffer_env, policy):
                 trajectories["z"][:, rollout_idx, time_idx] = agent_state["z"]
                 trajectories["heading"][:, rollout_idx, time_idx] = agent_state["heading"]
                 trajectories["id"][:, rollout_idx, time_idx] = agent_state["id"]
+                trajectories["dones"][:, rollout_idx, time_idx] = truncations
 
                 # Step policy
-                with torch.no_grad():
-                    ob_tensor = torch.as_tensor(obs).to(device)
-                    logits, value = policy.forward_eval(ob_tensor, state)
-                    action, logprob, _ = pufferlib.pytorch.sample_logits(logits)
-                    action_np = action.cpu().numpy().reshape(puffer_env.action_space.shape)
-
-                if isinstance(logits, torch.distributions.Normal):
-                    action_np = np.clip(action_np, puffer_env.action_space.low, puffer_env.action_space.high)
-
-                obs, _, _, _, _ = puffer_env.step(action_np)
+                if policy is None and actions is not None:
+                    human_act_time_index = self.init_steps + time_idx
+                    action_np = actions[human_act_time_index, :].copy()
+
+                    # Replace invalid actions (-1) with "do nothing" action
+                    # For discrete actions: use action 45 (accel=0, steer=0)
+                    # For continuous actions: use [0.0, 0.0]
+                    invalid_mask = action_np == -1.0
+
+                    if puffer_env.action_space.__class__.__name__ == "MultiDiscrete":
+                        # Discrete action space
+                        action_np[invalid_mask] = 45  # Do nothing action
+
+                elif policy is not None:
+                    if self.mode == "bc_policy":
+                        with torch.no_grad():
+                            ob_tensor = torch.as_tensor(obs).to(device)
+                            pred_action = policy(ob_tensor, deterministic=True)
+                            action_np = pred_action.cpu().numpy().reshape(puffer_env.action_space.shape)
+                    else:
+                        with torch.no_grad():
+                            ob_tensor = torch.as_tensor(obs).to(device)
+                            logits, value = policy.forward_eval(ob_tensor, state)
+                            action, logprob, _ = pufferlib.pytorch.sample_logits(logits)
+                            action_np = action.cpu().numpy().reshape(puffer_env.action_space.shape)
+
+                        if isinstance(logits, torch.distributions.Normal):
+                            action_np = np.clip(action_np, puffer_env.action_space.low, puffer_env.action_space.high)
+
+                obs, rewards, terminals, truncations, infos = puffer_env.step(action_np)
 
         return trajectories
 
@@ -229,6 +266,7 @@ def collect_wosac_random_baseline(self, puffer_env):
             "y": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32),
             "heading": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32),
             "id": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.int32),
+            "dones": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.bool_),
         }
 
         for rollout_idx in range(self.num_rollouts):
@@ -296,6 +334,7 @@ def compute_metrics(
         sim_x = simulated_trajectories["x"]
         sim_y = simulated_trajectories["y"]
         sim_heading = simulated_trajectories["heading"]
+        sim_dones = simulated_trajectories["dones"]
         ref_x = ground_truth_trajectories["x"]
         ref_y = ground_truth_trajectories["y"]
         ref_heading = ground_truth_trajectories["heading"]
@@ -313,6 +352,7 @@ def compute_metrics(
         eval_sim_heading = sim_heading[eval_mask]
         eval_ref_x = ref_x[eval_mask]
         eval_ref_y = ref_y[eval_mask]
+        eval_dones = sim_dones[eval_mask]
         eval_ref_heading = ref_heading[eval_mask]
         eval_ref_valid = ref_valid[eval_mask]
         eval_agent_length = agent_length[eval_mask]
@@ -532,8 +572,18 @@ def compute_metrics(
         # in the logged scenario, we need to filter possible collisions in simulation.
         # `sim_collision_indication` shape: (n_samples, n_objects).
 
-        sim_collision_indication = np.any(np.where(eval_ref_valid, sim_collision_per_step, False), axis=2)
-        ref_collision_indication = np.any(np.where(eval_ref_valid, ref_collision_per_step, False), axis=2)
+        # Combine validity masks: only count events when ref is valid and agent is not done
+        if self.filter_out_post_done:
+            active_mask = eval_ref_valid & ~eval_dones  # (n_agents, n_rollouts, n_steps)
+        else:
+            active_mask = eval_ref_valid  # (n_agents, 1, n_steps)
+
+        # Diagnostic: show average number of active timesteps per rollout
+        active_timesteps_per_rollout = np.sum(active_mask, axis=2)  # (n_agents, n_rollouts)
+        avg_active_timesteps = np.mean(active_timesteps_per_rollout)
+
+        sim_collision_indication = np.any(np.where(active_mask, sim_collision_per_step, False), axis=2)
+        ref_collision_indication = np.any(np.where(active_mask, ref_collision_per_step, False), axis=2)
 
         sim_num_collisions = np.mean(sim_collision_indication, axis=1)
         ref_num_collisions = np.mean(ref_collision_indication, axis=1)
@@ -548,8 +598,8 @@ def compute_metrics(
         )
 
         # Offroad likelihood (same pattern as collision)
-        sim_offroad_indication = np.any(np.where(eval_ref_valid, sim_offroad_per_step, False), axis=2)
-        ref_offroad_indication = np.any(np.where(eval_ref_valid, ref_offroad_per_step, False), axis=2)
+        sim_offroad_indication = np.any(np.where(active_mask, sim_offroad_per_step, False), axis=2)
+        ref_offroad_indication = np.any(np.where(active_mask, ref_offroad_per_step, False), axis=2)
 
         sim_num_offroad = np.mean(sim_offroad_indication, axis=1)
         ref_num_offroad = np.mean(ref_offroad_indication, axis=1)
@@ -784,21 +834,31 @@ def __init__(self, configs, logger=None):
         self.human_replay_stats = None
         self.sp_env = None
         self.hr_env = None
+        self.render_env_idx = 0  # Which of the vecenvs to use for rendering
+        self.inference_lambda_values = [0.0, 0.01, 0.05, 0.2]
+        self.lambda_sweep_results = {}  # {lambda_val: collision_rate}
 
         self._unpack_eval_configs(configs)
 
     def _unpack_eval_configs(self, configs):
         eval_config = copy.deepcopy(configs)
         # Create separate evaluation environments based on specified configs
-        eval_config["env"]["termination_mode"] = 0
+        eval_config["env"]["termination_mode"] = 1  # Important to ensure correct statistics
         backend = eval_config["eval"].get("backend", "PufferEnv")
         eval_config["env"]["map_dir"] = eval_config["eval"]["map_dir"]
+        eval_config["env"]["num_maps"] = 10_000  # Validation set
         eval_config["env"]["num_agents"] = eval_config["eval"]["num_eval_agents"]
         eval_config["env"]["episode_length"] = 91  # WOMD scenario length
         eval_config["vec"] = dict(backend=backend, num_envs=1)
+        eval_config["env"]["fix_lambdas"] = True
+        eval_config["env"]["fix_rewards"] = True  # Fix to the ini file ones for all agents
+        eval_config["env"]["lambda_value"] = configs["env"]["lambda_value"]
+        eval_config["env"]["obs_partner_noise_speed"] = 0.0
+        eval_config["env"]["obs_partner_noise_pos"] = 0.0
 
         self.hr_eval_config = copy.deepcopy(eval_config)
         self.hr_eval_config["env"]["control_mode"] = "control_sdc_only"
+        self.hr_eval_config["env"]["goal_behavior"] = 0  # Remove and terminate at goal
         self.sp_eval_config = copy.deepcopy(eval_config)
         self.sp_eval_config["env"]["control_mode"] = "control_agents"
         self.render_select_mode = self.configs["eval"]["render_select_mode"]
@@ -834,7 +894,12 @@ def select_render_env(self, env_logs):
         # Add other modes based on desiderata here
         return 0
 
-    def rollout(self, policy, mode="self_play"):
+    def rollout(self, policy, mode="self_play", view_mode=None):
+        from pufferlib.ocean.drive.drive import RenderView
+
+        if view_mode is None:
+            view_mode = RenderView.FULL_SIM_STATE
+
         env = self.hr_env if mode == "human_replay" else self.sp_env
         render_eval = self.render_sp_rollout if mode == "self_play" else self.render_hr_rollout
         driver = env.driver_env
@@ -847,24 +912,36 @@ def rollout(self, policy, mode="self_play"):
         else:
             render_env_idx = self.select_render_env([{}] * driver.num_envs)
 
-        info_list = self._run_rollout(policy, env, render_env_idx if render_eval else None)
+        env_statistics = self._run_rollout(
+            policy,
+            env,
+            mode,
+            render_env_idx if render_eval else None,
+            per_env_logs=True,
+            view_mode=view_mode,
+        )
 
-        final_info = info_list[0] if info_list else {}
         if mode == "self_play":
-            self.self_play_stats = final_info
-            self.self_play_stats["render_env_idx"] = render_env_idx
+            self.self_play_stats = env_statistics
+            self.self_play_stats[0]["render_env_idx"] = render_env_idx
         elif mode == "human_replay":
-            self.human_replay_stats = final_info
-            self.human_replay_stats["render_env_idx"] = render_env_idx
+            self.human_replay_stats = env_statistics
+            self.human_replay_stats[0]["render_env_idx"] = render_env_idx
 
-    def _run_rollout(self, policy, env, render_env_idx=None, per_env_logs=False):
+    def _run_rollout(self, policy, env, mode, render_env_idx=None, per_env_logs=False, view_mode=None):
         """Run a single rollout. If render_env_idx is not None, render that env."""
+        from pufferlib.ocean.drive.drive import RenderView
+
+        if view_mode is None:
+            view_mode = RenderView.FULL_SIM_STATE
+
         driver = env.driver_env
         num_agents = env.observation_space.shape[0]
         device = self.configs["train"]["device"]
 
         # Reset environment
         obs, info = env.reset()
+        terminals = np.zeros((num_agents, 1), dtype=bool)
 
         # Initialize RNN state if needed
         state = {}
@@ -876,8 +953,10 @@ def _run_rollout(self, policy, env, render_env_idx=None, per_env_logs=False):
 
         info_list = []
         for time_idx in range(self.sim_steps):
-            if render_env_idx is not None:
-                driver.render(env_id=render_env_idx)
+            if mode == "human_replay" and not terminals[render_env_idx]:
+                driver.render(view_mode=view_mode, env_idx=render_env_idx)
+            elif mode == "self_play":
+                driver.render(view_mode=view_mode, env_idx=render_env_idx)
 
             # Get action from policy
             with torch.no_grad():
@@ -891,13 +970,119 @@ def _run_rollout(self, policy, env, render_env_idx=None, per_env_logs=False):
                 action_np = np.clip(action_np, env.action_space.low, env.action_space.high)
 
             # Step environment
-            obs, rewards, dones, truncs, info_list = env.step(action_np, per_env_logs=per_env_logs)
+            obs, rewards, terminals, truncated, info_list = env.step(action_np, per_env_logs=per_env_logs)
 
-            if truncs.all():
+            if truncated.all():
                 break
 
         return info_list
 
+    def run_lambda_sweep(self, policy, load_env_fn_from_config):
+        """Run human replay rollouts across inference lambda values and collect stats.
+
+        Args:
+            policy: The policy to evaluate.
+            load_env_fn_from_config: Callable(config) that creates a new hr_env.
+        """
+        self.lambda_sweep_results = {}
+        for lam in self.inference_lambda_values:
+            config = copy.deepcopy(self.hr_eval_config)
+            config["env"]["fix_lambdas"] = True
+            config["env"]["lambda_value"] = lam
+
+            self.hr_env = load_env_fn_from_config(config)
+            self.rollout(policy, mode="human_replay")
+            self.hr_env.close()
+
+            if self.human_replay_stats is not None:
+                self.lambda_sweep_results[lam] = {
+                    "collision_rate": self.human_replay_stats.get("collision_rate", 0.0),
+                    "score": self.human_replay_stats.get("score", 0.0),
+                }
+            else:
+                self.lambda_sweep_results[lam] = {"collision_rate": 0.0, "score": 0.0}
+
+    def log_lambda_sweep(self, epoch):
+        """Log the lambda sweep results as scalar metrics and a seaborn swarmplot to wandb."""
+        if not (self.logger and hasattr(self.logger, "wandb") and self.logger.wandb):
+            return
+        if not self.lambda_sweep_results:
+            return
+        import wandb
+
+        df = pd.DataFrame(self.lambda_sweep_results).T
+        df.index.name = "lambda"
+
+        fig, ax = plt.subplots(1, 1, figsize=(4, 4), dpi=150)
+        ax.set_title(f"Effect of λ (regularization strength) \n Epoch {epoch}")
+        sns.lineplot(data=df, x="lambda", y="collision_rate", marker="s", color="tab:blue", ax=ax)
+        ax.set_ylabel("Human-replay collision rate", color="tab:blue")
+        ax.tick_params(axis="y", labelcolor="tab:blue")
+        ax.set_xlabel(r"$λ$")
+        ax.set_ylim(0, 0.5)
+
+        ax2 = ax.twinx()
+        sns.lineplot(data=df, x="lambda", y="score", marker="o", color="tab:orange", ax=ax2)
+        ax2.set_ylabel("Score", color="tab:orange")
+        ax2.tick_params(axis="y", labelcolor="tab:orange")
+        ax2.set_ylim(0, 1)
+
+        ax.grid(True, alpha=0.3)
+        fig.tight_layout()
+        self.logger.wandb.log({"eval/lambda_effect": wandb.Image(fig)})
+        plt.close(fig)
+
+    def run_collision_reward_sweep(self, policy, load_env_fn_from_config):
+        """Run human replay rollouts across collision reward values and collect stats.
+
+        Args:
+            policy: The policy to evaluate.
+            load_env_fn_from_config: Callable(config) that creates a new hr_env.
+        """
+        self.collision_reward_sweep_results = {}
+        self.inference_collision_reward_values = [0.1, 0.0, -0.1, -0.3]
+
+        for reward_val in self.inference_collision_reward_values:
+            config = copy.deepcopy(self.hr_eval_config)
+            config["env"]["fix_rewards"] = True
+            config["env"]["reward_vehicle_collision"] = reward_val
+
+            self.hr_env = load_env_fn_from_config(config)
+            self.rollout(policy, mode="human_replay")
+            self.hr_env.close()
+
+            if self.human_replay_stats is not None:
+                self.collision_reward_sweep_results[reward_val] = {
+                    "collision_rate": self.human_replay_stats["collision_rate"],
+                    "score": self.human_replay_stats.get("score", 0.0),
+                }
+            else:
+                self.collision_reward_sweep_results[reward_val] = {"collision_rate": 0.0, "score": 0.0}
+
+    def log_collision_reward_sweep(self, epoch):
+        """Log the collision reward sweep results as a plot to wandb."""
+        if not (self.logger and hasattr(self.logger, "wandb") and self.logger.wandb):
+            return
+        if not self.collision_reward_sweep_results:
+            return
+        import wandb
+
+        df = pd.DataFrame(self.collision_reward_sweep_results).T
+        df.index.name = "collision_reward"
+
+        fig, ax = plt.subplots(1, 1, figsize=(4, 4), dpi=150)
+        ax.set_title(f"Effect of collision penalty conditioning\nEpoch {epoch}")
+        sns.lineplot(data=df, x="collision_reward", y="collision_rate", marker="s", color="tab:purple", ax=ax)
+        ax.set_ylabel("Human-replay collision rate", color="tab:purple")
+        ax.tick_params(axis="y")
+        ax.set_xlabel("Collision penalty (conditioning value)")
+        ax.set_ylim(0, 1.0)
+
+        ax.grid(True, alpha=0.3)
+        fig.tight_layout()
+        self.logger.wandb.log({"eval/collision_reward_effect": wandb.Image(fig)})
+        plt.close(fig)
+
     def log_videos(self, eval_mode, epoch):
         """Log all mp4s in local path to wandb after env close has flushed ffmpeg pipes."""
         import os
@@ -926,20 +1111,26 @@ def log_videos(self, eval_mode, epoch):
         for p in video_files:
             os.remove(p)
 
-    def log_stats(self):
-        if not (self.logger and hasattr(self.logger, "wandb") and self.logger.wandb):
-            return
-
-        eval_stats = {}
+    def collect_stats(self):
+        stats = {}
 
         if self.human_replay_stats is not None:
-            eval_stats["eval/hr_collision_rate"] = self.human_replay_stats["collision_rate"]
-            eval_stats["eval/hr_score"] = self.human_replay_stats["score"]
-        if self.self_play_stats is not None:
-            eval_stats["eval/sp_collision_rate"] = self.self_play_stats["collision_rate"]
-            eval_stats["eval/sp_score"] = self.self_play_stats["score"]
-            eval_stats["eval/num_agents"] = self.self_play_stats["n"]
-        else:
-            return
+            populated = [log for log in self.human_replay_stats if log and log.get("n", 0) > 0]
+            if populated:
+                collisions_per_agent = np.array([log["collisions_per_agent"] for log in populated])
+                did_collide = np.array([log["collision_rate"] for log in populated])
+                stats["eval/hr_mean_collisions_per_agent"] = float(np.mean(collisions_per_agent))
+                stats["eval/hr_mean_did_collide"] = float(np.mean(did_collide))
+                stats["eval/hr_score"] = float(np.mean([log["score"] for log in populated]))
 
-        self.logger.wandb.log(eval_stats)
+        if self.self_play_stats is not None:
+            populated = [log for log in self.self_play_stats if log and log.get("n", 0) > 0]
+            if populated:
+                collisions_per_agent = np.array([log["collisions_per_agent"] for log in populated])
+                did_collide = np.array([log["collision_rate"] for log in populated])
+                stats["eval/sp_mean_collisions_per_agent"] = float(np.mean(collisions_per_agent))
+                stats["eval/sp_mean_did_collide"] = float(np.mean(did_collide))
+                stats["eval/sp_score"] = float(np.mean([log["score"] for log in populated]))
+                stats["eval/sp_num_agents"] = float(np.mean([log["n"] for log in populated]))
+
+        return stats
diff --git a/pufferlib/ocean/benchmark/evaluator_minimal.py b/pufferlib/ocean/benchmark/evaluator_minimal.py
new file mode 100644
index 0000000000..8c3cb8903b
--- /dev/null
+++ b/pufferlib/ocean/benchmark/evaluator_minimal.py
@@ -0,0 +1,82 @@
+"""Standalone evaluator for checkpoint benchmarking."""
+
+import torch
+import numpy as np
+import pufferlib
+
+
+class CheckpointEvaluator:
+    """Runs policy rollouts in a puffer environment.
+
+    The caller is responsible for constructing the config and environment
+    with the desired control_mode, goal_behavior, etc. This class just
+    handles the rollout loop.
+    """
+
+    def __init__(self, configs):
+        self.configs = configs
+
+    def rollout(self, policy, env, render_env_idx=None, view_mode=None, deterministic=False):
+        """Run a single rollout and return the per-env info logs.
+
+        Args:
+            policy: The policy to evaluate.
+            env: A puffer vecenv, already configured.
+            render_env_idx: If not None, render this env index each step.
+                The env's render_mode should be set to 1 (headless/ffmpeg)
+                or 0 (window) accordingly.
+            view_mode: RenderView enum value. Defaults to FULL_SIM_STATE.
+        """
+        from pufferlib.ocean.drive.drive import RenderView
+
+        if view_mode is None:
+            view_mode = RenderView.FULL_SIM_STATE
+
+        driver = env.driver_env
+        num_agents = env.observation_space.shape[0]
+        device = self.configs["train"]["device"]
+
+        obs, info = env.reset()
+        terminals = np.zeros((num_agents, 1), dtype=bool)
+
+        state = {}
+        if self.configs["train"]["use_rnn"]:
+            state = dict(
+                lstm_h=torch.zeros(num_agents, policy.hidden_size, device=device),
+                lstm_c=torch.zeros(num_agents, policy.hidden_size, device=device),
+            )
+
+        info_list = []
+        for time_idx in range(env.episode_length):
+            if render_env_idx is not None:
+                cur = driver.agent_offsets[render_env_idx]
+                render_env_done = terminals[cur].all()
+                if not render_env_done:
+                    driver.render(view_mode=view_mode, env_idx=render_env_idx)
+
+            with torch.no_grad():
+                ob_tensor = torch.as_tensor(obs).to(device)
+                logits, value = policy.forward_eval(ob_tensor, state)
+
+                if deterministic:
+                    # Greedy: pick highest-probability action per head
+                    if isinstance(logits, torch.Tensor):
+                        action = logits.argmax(dim=-1, keepdim=True).int()
+                    elif isinstance(logits, torch.distributions.Normal):
+                        action = logits.loc  # mean of the distribution
+                    else:  # multi-discrete (list of logit tensors)
+                        action = torch.stack([l.argmax(dim=-1) for l in logits], dim=-1).int()
+                else:
+                    action, logprob, _ = pufferlib.pytorch.sample_logits(logits)
+
+                action_np = action.cpu().numpy().reshape(env.action_space.shape)
+
+            if isinstance(logits, torch.distributions.Normal):
+                action_np = np.clip(action_np, env.action_space.low, env.action_space.high)
+
+            obs, rewards, terminals, truncated, info_list = env.step(action_np, per_env_logs=True)
+
+            if truncated.all():
+                break
+
+        return info_list
diff --git a/pufferlib/ocean/benchmark/plot_and_format.py b/pufferlib/ocean/benchmark/plot_and_format.py
new file mode 100644
index 0000000000..df6ac9c380
--- /dev/null
+++ b/pufferlib/ocean/benchmark/plot_and_format.py
@@ -0,0 +1,706 @@
+"""Plotting functions for checkpoint evaluation results."""
+
+import re
+import warnings
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mticker
+import numpy as np
+import seaborn as sns
+
+DPI = 600
+
+
+def _set_style(n_policies):
+    sns.set("notebook", font_scale=1.05, rc={"figure.figsize": (16, 5)})
+    sns.set_style("ticks", rc={"figure.facecolor": "none", "axes.facecolor": "none"})
+    warnings.filterwarnings("ignore")
+    plt.set_loglevel("WARNING")
+    mpl.rcParams["lines.markersize"] = 8
+    return sns.color_palette("colorblind", n_colors=n_policies)
+
+
+def _short_name(checkpoint_path):
+    """Extract a short label from a checkpoint path."""
+    return checkpoint_path.split("/")[-1].replace(".pt", "")
+
+
+def _draw_upper_bound(ax, value, label="upper bound"):
+    """Draw a lightgreen striped line indicating an upper bound (e.g. perfect score)."""
+    ax.axhline(value, color="lightgreen", linestyle=(0, (5, 2, 1, 2)), linewidth=1.5, alpha=0.9, label=label)
+
+
+def _draw_lower_bound(ax, value, label="lower bound"):
+    """Draw a purple dashed line indicating a lower bound (e.g. zero collisions)."""
+    ax.axhline(value, color="purple", linestyle="--", linewidth=1.5, alpha=0.7, label=label)
+
+
+def _format_percent(ax):
+    """Format y-axis as percentages and clip at 0."""
+    ax.set_ylim(bottom=0)
+
+
+def _fmt_maps(n):
+    """Format a map count as a human-readable label: 10 -> '10', 1000 -> '1k', 50000 -> '50k'."""
+    if n >= 1000 and n % 1000 == 0:
+        return f"{n // 1000}k"
+    return str(int(n))
+
+
+def _maps_to_human_time(maps):
+    """Convert map count to human-readable time: minutes = (maps * 9 * 5) / 60."""
+    minutes = (maps * 9 * 5) / 60
+    if minutes >= 60:
+        hours = minutes / 60
+        if hours == int(hours):
+            return f"{int(hours)} hour{'s' if int(hours) != 1 else ''}"
+        return f"{hours:.1f} hours"
+    if minutes == int(minutes):
+        return f"{int(minutes)} minutes"
+    return f"{minutes:.1f} minutes"
+
+
+def plot_scores(df, save_path="eval_scores.pdf"):
+    """Figure 1: Self-play and human-replay scores on validation sets.
+
+    Three columns:
+      1) Self-play score (sp_val)
+      2) Human-replay score on full validation (hr_val)
+      3) Human-replay score on interactive scenes (hr_interactive)
+    """
+    df = df.copy()
+    df["policy"] = df["checkpoint"].apply(_short_name)
+    palette = _set_style(df["policy"].nunique())
+
+    fig, axes = plt.subplots(1, 3, figsize=(15, 5), sharey=True)
+
+    modes = ["sp_val", "hr_val", "hr_interactive"]
+    titles = [
+        "Self-play score (validation)",
+        "Human-replay score (validation)",
+        "Human-replay score (interactive)",
+    ]
+
+    for ax, mode, title in zip(axes, modes, titles):
+        subset = df[df["mode"] == mode]
+        sns.barplot(data=subset, x="policy", y="score", errorbar="se", palette=palette, ax=ax, alpha=0.8)
+        _draw_upper_bound(ax, 1.0, label="perfect score")
+        ax.set_title(title)
+        ax.set_xlabel("")
+        ax.grid(axis="y", alpha=0.3, linestyle="--")
+        ax.tick_params(axis="x", rotation=30)
+        ax.legend(fontsize=8, loc="lower right")
+        _format_percent(ax)
+        sns.despine(ax=ax)
+
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white")
+    plt.show()
+    return fig
+
+
+def _prepare_scaling_metadata(df):
+    """Shared preprocessing for scaling plots. Returns None if data is missing."""
+    scaling_modes = ["scaling_sp_val", "scaling_hr_interactive"]
+    scaling_df = df[df["mode"].isin(scaling_modes)].copy()
+    if scaling_df.empty:
+        return None
+    if "sp_maps" not in scaling_df.columns:
+        return None
+
+    scaling_df["collision_rate_pct"] = scaling_df["collision_rate"] * 100
+    return scaling_df
+
+
+def _scaling_scatter_common(
+    agg, subplot_specs, anchor_vals, color_map, marker_map, figsize, save_path, clip_y_zero=True, ref_lines=None
+):
+    """Shared logic for scaling scatter plots.
+
+    Args:
+        ref_lines: Optional list (one per subplot) of lists of (value, color, linestyle, label) tuples
+                   to draw as horizontal reference lines.
+    """
+    _set_style(len(anchor_vals))
+    fig, axes = plt.subplots(1, len(subplot_specs), figsize=figsize)
+    if len(subplot_specs) == 1:
+        axes = [axes]
+
+    for ax, (mode, y_col, yerr_col, ylabel, title) in zip(axes, subplot_specs):
+        mode_agg = agg[agg["mode"] == mode] if "mode" in agg.columns else agg
+        for anchor_val in anchor_vals:
+            grp = mode_agg[mode_agg["anchor_maps"] == anchor_val].sort_values("sp_maps")
+            if grp.empty:
+                continue
+            label = grp["anchor_label"].iloc[0]
+            ax.errorbar(
+                grp["sp_maps"],
+                grp[y_col],
+                yerr=grp[yerr_col],
+                marker=marker_map[anchor_val],
+                color=color_map[anchor_val],
+                capsize=3,
+                linewidth=1.5,
+                markersize=8,
+                label=label,
+            )
+        ax.set_xscale("log")
+        ax.xaxis.set_major_formatter(
+            mticker.FuncFormatter(lambda x, _: f"{int(x / 1000)}k" if x >= 1000 else str(int(x)))
+        )
+        ax.set_xlabel("Self-play training maps")
+        ax.set_ylabel(ylabel)
+        ax.set_title(title)
+        if clip_y_zero:
+            ax.set_ylim(bottom=0)
+        if ref_lines is not None:
+            subplot_idx = list(axes).index(ax)
+            for value, color, linestyle, label in ref_lines[subplot_idx]:
+                ax.axhline(value, color=color, linestyle=linestyle, linewidth=1.5, alpha=0.7, label=label)
+        ax.grid(axis="y", alpha=0.3, linestyle="--")
+        ax.legend(fontsize=9, title="Anchor data")
+        sns.despine(ax=ax)
+
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white")
+    plt.show()
+    return fig
+
+
+def _build_anchor_style_maps(anchor_vals):
+    """Build consistent color and marker maps for anchor values."""
+    palette = sns.color_palette("colorblind", n_colors=len(anchor_vals))
+    color_map = {v: palette[i] for i, v in enumerate(anchor_vals)}
+    markers = ["X", "o", "s", "D", "^", "v", "P", "*"]
+    marker_map = {v: markers[i % len(markers)] for i, v in enumerate(anchor_vals)}
+    return color_map, marker_map
+
+
+def plot_scaling_scatter(df, save_path="eval_scaling_scatter.pdf"):
+    """Scaling scatter plot: 4-column layout.
+
+    x-axis: self-play training maps (sp_maps), log-scaled
+    color/shape: anchor maps
+
+    Subplots:
+      0) Self-play score (validation)
+      1) Self-play collision rate (validation)
+      2) Human-replay collision rate (interactive)
+      3) Human-replay at-fault collision rate (interactive)
+    """
+    scaling_df = _prepare_scaling_metadata(df)
+    if scaling_df is None:
+        print("  No scaling data found — skipping plot_scaling_scatter.")
+        return None
+
+    scaling_df["anchor_maps"] = scaling_df["anchor_maps"].fillna(0).astype(int)
+    scaling_df["at_fault_collision_rate_pct"] = scaling_df["at_fault_collision_rate"] * 100
+
+    if "dynamics" not in scaling_df.columns:
+        scaling_df["dynamics"] = "delta"
+
+    agg = (
+        scaling_df.groupby(["sp_maps", "anchor_maps", "dynamics", "mode"])[
+            ["collision_rate_pct", "score", "at_fault_collision_rate_pct"]
+        ]
+        .agg(["mean", "sem"])
+        .reset_index()
+    )
+    agg.columns = [
+        "sp_maps",
+        "anchor_maps",
+        "dynamics",
+        "mode",
+        "coll_mean",
+        "coll_sem",
+        "score_mean",
+        "score_sem",
+        "at_fault_coll_mean",
+        "at_fault_coll_sem",
+    ]
+
+    agg["series_key"] = agg.apply(lambda r: f"{r['dynamics']}_anchor{r['anchor_maps']}", axis=1)
+    agg["anchor_label"] = agg.apply(
+        lambda r: (
+            f"regularized, anchor with {_maps_to_human_time(r['anchor_maps'])} of human data"
+            if r["anchor_maps"] > 0
+            else "unregularized"
+        ),
+        axis=1,
+    )
+
+    series_keys = sorted(agg["series_key"].unique())
+
+    # Assign colors: orange shades for regularized, blue/green for unregularized
+    unreg_colors = ["#1f77b4", "#2ca02c", "#17becf", "#9467bd"]
+    reg_colors = ["#ff7f0e", "#d62728", "#e377c2", "#bcbd22"]
+
+    unreg_keys = [k for k in series_keys if "_anchor0" in k]
+    reg_keys = [k for k in series_keys if "_anchor0" not in k]
+
+    color_map = {}
+    for i, k in enumerate(unreg_keys):
+        color_map[k] = unreg_colors[i % len(unreg_colors)]
+    for i, k in enumerate(reg_keys):
+        color_map[k] = reg_colors[i % len(reg_colors)]
+
+    markers = ["X", "o", "s", "D", "^", "v", "P", "*"]
+    marker_map = {k: markers[i % len(markers)] for i, k in enumerate(series_keys)}
+
+    # (mode, y_col, yerr_col, ylabel, title)
+    subplot_specs = [
+        ("scaling_sp_val", "score_mean", "score_sem", "Score", "Self-play score — validation"),
+        ("scaling_sp_val", "coll_mean", "coll_sem", "Collision rate (%)", "Self-play collision rate (%) — validation"),
+        (
+            "scaling_hr_interactive",
+            "coll_mean",
+            "coll_sem",
+            "Collision rate (%)",
+            "Human-replay collision rate (%) — interactive",
+        ),
+        (
+            "scaling_hr_interactive",
+            "at_fault_coll_mean",
+            "at_fault_coll_sem",
+            "At-fault collision rate (%)",
+            "Human-replay at-fault collision rate (%) — interactive",
+        ),
+    ]
+
+    _set_style(len(series_keys))
+    fig, axes = plt.subplots(1, len(subplot_specs), figsize=(24, 5))
+
+    for ax, (mode, y_col, yerr_col, ylabel, title) in zip(axes, subplot_specs):
+        mode_agg = agg[agg["mode"] == mode]
+        for sk in series_keys:
+            grp = mode_agg[mode_agg["series_key"] == sk].sort_values("sp_maps")
+            if grp.empty:
+                continue
+            label = grp["anchor_label"].iloc[0]
+            ax.errorbar(
+                grp["sp_maps"],
+                grp[y_col],
+                yerr=grp[yerr_col],
+                marker=marker_map[sk],
+                color=color_map[sk],
+                capsize=3,
+                linewidth=1.5,
+                markersize=8,
+                label=label,
+            )
+        ax.set_xscale("log")
+        ax.xaxis.set_major_formatter(
+            mticker.FuncFormatter(lambda x, _: f"{int(x / 1000)}k" if x >= 1000 else str(int(x)))
+        )
+        ax.set_xlabel("Self-play training maps")
+        ax.set_ylabel(ylabel)
+        ax.set_title(title)
+        ax.set_ylim(bottom=0)
+        if y_col == "score_mean":
+            _draw_upper_bound(ax, 1.0, label="perfect score")
+        ax.grid(axis="y", alpha=0.3, linestyle="--")
+        ax.legend(fontsize=8, title="Policy")
+        sns.despine(ax=ax)
+
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white")
+    plt.show()
+    return fig
+
+
+def plot_scaling_barplot(df, save_path="eval_scaling_barplot.pdf"):
+    """Bar plots for 50k self-play checkpoints on training set: score, collision rate, offroad rate."""
+    scaling_df = df[df["mode"] == "scaling_sp_train"].copy()
+    if scaling_df.empty:
+        print("  No scaling_sp_train data found — skipping plot_scaling_barplot.")
+        return None
+
+    scaling_df["anchor_maps"] = scaling_df["anchor_maps"].fillna(0).astype(int)
+    if "dynamics" not in scaling_df.columns:
+        scaling_df["dynamics"] = "delta"
+
+    # Filter to only 50k self-play checkpoints
+    scaling_df = scaling_df[scaling_df["sp_maps"] == 50000]
+    if scaling_df.empty:
+        print("  No 50k sp_maps data found — skipping plot_scaling_barplot.")
+        return None
+
+    scaling_df["collision_rate_pct"] = scaling_df["collision_rate"] * 100
+    scaling_df["offroad_rate_pct"] = scaling_df["offroad_rate"] * 100
+
+    scaling_df["policy"] = scaling_df.apply(
+        lambda r: (
+            f"regularized, anchor with {_maps_to_human_time(r['anchor_maps'])} of human data"
+            if r["anchor_maps"] > 0
+            else "unregularized"
+        ),
+        axis=1,
+    )
+
+    palette = _set_style(scaling_df["policy"].nunique())
+
+    subplot_specs = [
+        ("score", "Score", "Self-play score — training, 50k maps"),
+        ("collision_rate_pct", "Collision rate (%)", "Self-play collision rate (%) — training, 50k maps"),
+        ("offroad_rate_pct", "Offroad rate (%)", "Self-play offroad rate (%) — training, 50k maps"),
+    ]
+
+    fig, axes = plt.subplots(1, 3, figsize=(18, 5))
+
+    for ax, (y_col, ylabel, title) in zip(axes, subplot_specs):
+        sns.barplot(data=scaling_df, x="policy", y=y_col, errorbar="se", palette=palette, ax=ax, alpha=0.8)
+        ax.set_title(title)
+        ax.set_xlabel("")
+        ax.set_ylabel(ylabel)
+        ax.set_ylim(bottom=0)
+        if y_col == "score":
+            _draw_upper_bound(ax, 1.0, label="perfect score")
+        ax.grid(axis="y", alpha=0.3, linestyle="--")
+        ax.tick_params(axis="x", rotation=30)
+        sns.despine(ax=ax)
+
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white")
+    plt.show()
+    return fig
+
+
+def plot_scaling_wosac(wosac_df, save_path="eval_scaling_wosac.pdf"):
+    """WOSAC scaling figure: 4-column scatter plot.
+
+    x-axis: self-play training maps (sp_maps), log-scaled
+    color:  anchor maps; unreg → 0
+    shape:  anchor maps
+
+    Subplots: realism_meta_score, kinematic_metrics, interactive_metrics, map_based_metrics
+    """
+    if wosac_df is None or wosac_df.empty:
+        print("  No WOSAC data found — skipping plot_scaling_wosac.")
+        return None
+
+    wdf = wosac_df.copy()
+    wdf["anchor_maps"] = wdf["anchor_maps"].fillna(0).astype(int)
+
+    wosac_metrics = [
+        "realism_meta_score",
+        "kinematic_metrics",
+        "interactive_metrics",
+        "map_based_metrics",
+    ]
+    agg = wdf.groupby(["sp_maps", "anchor_maps"])[wosac_metrics].agg(["mean", "sem"]).reset_index()
+    # Flatten multi-level columns
+    flat_cols = ["sp_maps", "anchor_maps"]
+    for m in wosac_metrics:
+        flat_cols.extend([f"{m}_mean", f"{m}_sem"])
+    agg.columns = flat_cols
+
+    agg["anchor_label"] = agg["anchor_maps"].apply(
+        lambda v: f"anchor = {_fmt_maps(v)}" if v > 0 else "no anchor (unreg)"
+    )
+
+    anchor_vals = sorted(agg["anchor_maps"].unique())
+    color_map, marker_map = _build_anchor_style_maps(anchor_vals)
+
+    titles = [
+        "Realism meta-score",
+        "Kinematic metrics",
+        "Interactive metrics",
+        "Map-based metrics",
+    ]
+    subplot_specs = [(None, f"{m}_mean", f"{m}_sem", t, t) for m, t in zip(wosac_metrics, titles)]
+
+    # Reference baselines per subplot (realism, kinematic, interactive, map)
+    smart_scores = [0.7818, 0.5200, 0.8914, 0.8378]
+    random_scores = [0.4459, 0.0506, 0.34, 0.4704]
+    ref_lines = [
+        [
+            (smart, "green", "--", "SMART"),
+            (rand, "grey", "--", "Random"),
+        ]
+        for smart, rand in zip(smart_scores, random_scores)
+    ]
+
+    return _scaling_scatter_common(
+        agg,
+        subplot_specs,
+        anchor_vals,
+        color_map,
+        marker_map,
+        figsize=(22, 5),
+        save_path=save_path,
+        clip_y_zero=False,
+        ref_lines=ref_lines,
+    )
+
+
+def generate_scaling_latex_table(df, save_path="eval_scaling_table.tex"):
+    """Generate a LaTeX-formatted table with heatmap coloring.
+
+    Score columns use a Greens colormap (darker = higher = better).
+    Collision rate columns use a Reds colormap (lighter = lower = better).
+
+    Required LaTeX packages:
+      \\usepackage{booktabs}
+      \\usepackage[table]{xcolor}
+      \\usepackage{graphicx}   % for \\resizebox
+
+    Rows:   one per (sp_maps, anchor_maps) configuration
+    Columns:
+      - Self-play maps (metadata), Anchor data
+      - Self-play (validation): score, collision rate, at-fault collision rate
+      - Human-replay (interactive): score, collision rate, at-fault collision rate
+    """
+    scaling_modes = ["scaling_sp_val", "scaling_hr_interactive"]
+    scaling_df = df[df["mode"].isin(scaling_modes)].copy()
+    if scaling_df.empty:
+        print("  No scaling data found — skipping generate_scaling_latex_table.")
+        return None
+
+    scaling_df["anchor_maps"] = scaling_df["anchor_maps"].fillna(0).astype(int)
+
+    metrics = ["score", "collision_rate", "at_fault_collision_rate"]
+    available_metrics = [m for m in metrics if m in scaling_df.columns]
+
+    agg = scaling_df.groupby(["sp_maps", "anchor_maps", "mode"])[available_metrics].agg(["mean", "sem"]).reset_index()
+    flat_cols = ["sp_maps", "anchor_maps", "mode"]
+    for m in available_metrics:
+        flat_cols.extend([f"{m}_mean", f"{m}_sem"])
+    agg.columns = flat_cols
+
+    sp = agg[agg["mode"] == "scaling_sp_val"].drop(columns=["mode"]).copy()
+    hr = agg[agg["mode"] == "scaling_hr_interactive"].drop(columns=["mode"]).copy()
+
+    sp_rename = {c: f"sp_{c}" for c in sp.columns if c not in ("sp_maps", "anchor_maps")}
+    hr_rename = {c: f"hr_{c}" for c in hr.columns if c not in ("sp_maps", "anchor_maps")}
+    sp = sp.rename(columns=sp_rename)
+    hr = hr.rename(columns=hr_rename)
+
+    merged = sp.merge(hr, on=["sp_maps", "anchor_maps"], how="outer")
+    merged = merged.sort_values(["sp_maps", "anchor_maps"]).reset_index(drop=True)
+
+    has_at_fault = "at_fault_collision_rate" in available_metrics
+
+    # --- Collect all values for normalization ---
+    score_cols = ["sp_score_mean", "hr_score_mean"]
+    coll_cols = ["sp_collision_rate_mean", "hr_collision_rate_mean"]
+    if has_at_fault:
+        coll_cols += ["sp_at_fault_collision_rate_mean", "hr_at_fault_collision_rate_mean"]
+
+    all_scores = merged[score_cols].values.flatten()
+    all_scores = all_scores[~np.isnan(all_scores)]
+    all_colls = merged[coll_cols].values.flatten()
+    all_colls = all_colls[~np.isnan(all_colls)]
+
+    score_min, score_max = (all_scores.min(), all_scores.max()) if len(all_scores) > 0 else (0, 1)
+    coll_min, coll_max = (all_colls.min(), all_colls.max()) if len(all_colls) > 0 else (0, 1)
+
+    def _score_intensity(val):
+        """Map score to green intensity 5–50 (darker = better = higher score)."""
+        if np.isnan(val):
+            return 0
+        if score_max == score_min:
+            return 25
+        t = (val - score_min) / (score_max - score_min)
+        return int(5 + t * 45)
+
+    def _coll_intensity(val):
+        """Map collision rate to red intensity 5–50 (lighter = better = lower rate)."""
+        if np.isnan(val):
+            return 0
+        if coll_max == coll_min:
+            return 25
+        t = (val - coll_min) / (coll_max - coll_min)
+        return int(5 + t * 45)
+
+    def _fmt_score(mean, sem, is_best=False):
+        """Format score with green cellcolor. Bold if best in column."""
+        if np.isnan(mean):
+            return "---"
+        intensity = _score_intensity(mean)
+        if not (np.isnan(sem) or sem == 0):
+            if is_best:
+                text = f"$\\bm{{{mean:.3f} \\pm {sem:.3f}}}$"
+            else:
+                text = f"${mean:.3f} \\pm {sem:.3f}$"
+        else:
+            text = f"\\textbf{{{mean:.3f}}}" if is_best else f"{mean:.3f}"
+        return f"\\cellcolor{{green!{intensity}}} {text}"
+
+    def _fmt_coll(mean, sem, is_best=False):
+        """Format collision rate (as %) with red cellcolor. Bold if best in column."""
+        if np.isnan(mean):
+            return "---"
+        intensity = _coll_intensity(mean)
+        m_pct, s_pct = mean * 100, sem * 100
+        if not (np.isnan(s_pct) or s_pct == 0):
+            if is_best:
+                text = f"$\\bm{{{m_pct:.1f} \\pm {s_pct:.1f}}}$"
+            else:
+                text = f"${m_pct:.1f} \\pm {s_pct:.1f}$"
+        else:
+            text = f"\\textbf{{{m_pct:.1f}}}" if is_best else f"{m_pct:.1f}"
+        return f"\\cellcolor{{red!{intensity}}} {text}"
+
+    def _anchor_label(anchor_maps):
+        if anchor_maps == 0:
+            return "0 (unreg.)"
+        return _maps_to_human_time(anchor_maps)
+
+    # --- Build LaTeX ---
+    n_metric_cols = 2 + int(has_at_fault)
+    col_spec = "rr" + "|" + "r" * n_metric_cols + "|" + "r" * n_metric_cols
+
+    lines = []
+    lines.append(
+        r"% Requires: \usepackage{booktabs}, \usepackage[table]{xcolor}, \usepackage{graphicx}, \usepackage{makecell}, \usepackage{bm}"
+    )
+    lines.append(r"\begin{table}[ht]")
+    lines.append(r"\centering")
+    lines.append(r"\caption{Scaling evaluation results on held-out test maps.}")
+    lines.append(r"\label{tab:scaling_results}")
+    lines.append(r"\resizebox{\textwidth}{!}{%")
+    lines.append(r"\begin{tabular}{" + col_spec + "}")
+    lines.append(r"\toprule")
+
+    sp_header = r"\multicolumn{" + str(n_metric_cols) + r"}{c|}{Self-play (test)}"
+    hr_header = r"\multicolumn{" + str(n_metric_cols) + r"}{c}{Human-replay (test)}"
+    lines.append(r" & & " + sp_header + " & " + hr_header + r" \\")
+
+    metric_headers = ["Score", "Coll. (\\%)"]
+    if has_at_fault:
+        metric_headers.append("At-fault (\\%)")
+    header2 = (
+        "\\makecell{Self-play maps \\\\ (metadata)} & Anchor data & "
+        + " & ".join(metric_headers)
+        + " & "
+        + " & ".join(metric_headers)
+        + r" \\"
+    )
+    lines.append(header2)
+    lines.append(r"\midrule")
+
+    # --- Compute best value per column (highest score, lowest collision rate) ---
+    best = {}
+    for col in score_cols:
+        if col in merged.columns:
+            best[col] = merged[col].max()  # higher is better
+    for col in coll_cols:
+        if col in merged.columns:
+            best[col] = merged[col].min()  # lower is better
+
+    def _is_best(col, val):
+        if col not in best or np.isnan(val):
+            return False
+        return np.isclose(val, best[col])
+
+    for _, row in merged.iterrows():
+        sp_maps_str = _fmt_maps(int(row["sp_maps"]))
+        anchor_str = _anchor_label(int(row["anchor_maps"]))
+        cells = [sp_maps_str, anchor_str]
+
+        # Self-play
+        cells.append(
+            _fmt_score(
+                row.get("sp_score_mean", np.nan),
+                row.get("sp_score_sem", np.nan),
+                is_best=_is_best("sp_score_mean", row.get("sp_score_mean", np.nan)),
+            )
+        )
+        cells.append(
+            _fmt_coll(
+                row.get("sp_collision_rate_mean", np.nan),
+                row.get("sp_collision_rate_sem", np.nan),
+                is_best=_is_best("sp_collision_rate_mean", row.get("sp_collision_rate_mean", np.nan)),
+            )
+        )
+        if has_at_fault:
+            cells.append(
+                _fmt_coll(
+                    row.get("sp_at_fault_collision_rate_mean", np.nan),
+                    row.get("sp_at_fault_collision_rate_sem", np.nan),
+                    is_best=_is_best(
+                        "sp_at_fault_collision_rate_mean", row.get("sp_at_fault_collision_rate_mean", np.nan)
+                    ),
+                )
+            )
+
+        # Human-replay
+        cells.append(
+            _fmt_score(
+                row.get("hr_score_mean", np.nan),
+                row.get("hr_score_sem", np.nan),
+                is_best=_is_best("hr_score_mean", row.get("hr_score_mean", np.nan)),
+            )
+        )
+        cells.append(
+            _fmt_coll(
+                row.get("hr_collision_rate_mean", np.nan),
+                row.get("hr_collision_rate_sem", np.nan),
+                is_best=_is_best("hr_collision_rate_mean", row.get("hr_collision_rate_mean", np.nan)),
+            )
+        )
+        if has_at_fault:
+            cells.append(
+                _fmt_coll(
+                    row.get("hr_at_fault_collision_rate_mean", np.nan),
+                    row.get("hr_at_fault_collision_rate_sem", np.nan),
+                    is_best=_is_best(
+                        "hr_at_fault_collision_rate_mean", row.get("hr_at_fault_collision_rate_mean", np.nan)
+                    ),
+                )
+            )
+
+        lines.append(" & ".join(cells) + r" \\")
+
+    lines.append(r"\bottomrule")
+    lines.append(r"\end{tabular}}")
+    lines.append(r"\end{table}")
+
+    latex_str = "\n".join(lines)
+
+    with open(save_path, "w") as f:
+        f.write(latex_str)
+
+    print(f"  LaTeX table written to {save_path}")
+    return latex_str
+
+
+def make_all_figures(df=None, wosac_df=None):
+    """Generate all evaluation figures."""
+    print("\nGenerating figures...")
+    if df is not None and not df.empty:
+        plot_scores(df)
+        print("  Saved eval_scores.pdf")
+        plot_scaling_barplot(df)
+        print("  Saved eval_scaling_barplot.pdf")
+        plot_scaling_scatter(df)
+        print("  Saved eval_scaling_scatter.pdf")
+        generate_scaling_latex_table(df)
+    plot_scaling_wosac(wosac_df)
+    print("  Saved eval_scaling_wosac.pdf")
+
+
+if __name__ == "__main__":
+    import os
+    import pandas as pd
+
+    EVAL_CSV = "checkpoint_eval_results.csv"
+    WOSAC_CSV = "checkpoint_wosac_results.csv"
+
+    df = None
+    wosac_df = None
+
+    if os.path.exists(EVAL_CSV):
+        df = pd.read_csv(EVAL_CSV)
+        print(f"Loaded {EVAL_CSV} ({len(df)} rows)")
+    else:
+        print(f"{EVAL_CSV} not found — skipping standard eval figures.")
+
+    if os.path.exists(WOSAC_CSV):
+        wosac_df = pd.read_csv(WOSAC_CSV)
+        print(f"Loaded {WOSAC_CSV} ({len(wosac_df)} rows)")
+    else:
+        print(f"{WOSAC_CSV} not found — skipping WOSAC figures.")
+
+    make_all_figures(df, wosac_df)
diff --git a/pufferlib/ocean/drive/binding.c b/pufferlib/ocean/drive/binding.c
index e86dad727a..d7a37ca872 100644
--- a/pufferlib/ocean/drive/binding.c
+++ b/pufferlib/ocean/drive/binding.c
@@ -68,6 +68,8 @@ static int my_put(Env *env, PyObject *args, PyObject *kwargs) {
 }
 
 static PyObject *my_shared(PyObject *self, PyObject *args, PyObject *kwargs) {
+    // This function samples maps until we have enough agents to fill the batch.
+    // It returns a tuple of (agent_offsets, map_ids, env_count).
     char *map_dir = unpack_str(kwargs, "map_dir");
     int num_agents = unpack(kwargs, "num_agents");
     int num_maps = unpack(kwargs, "num_maps");
@@ -78,23 +80,26 @@ static PyObject *my_shared(PyObject *self, PyObject *args, PyObject *kwargs) {
     float goal_target_distance = unpack(kwargs, "goal_target_distance");
     int max_controlled_agents = unpack(kwargs, "max_controlled_agents");
 
-    clock_gettime(CLOCK_REALTIME, &ts);
-    srand(ts.tv_nsec); // Always use random sampling with replacement
+    // Deterministic map sampling
+    int seed = unpack(kwargs, "seed");
+    srand(seed);
 
     int total_agent_count = 0;
     int env_count = 0;
-
     int max_envs = num_agents;
-
     int maps_checked = 0;
+
     PyObject *agent_offsets = PyList_New(max_envs + 1);
     PyObject *map_ids = PyList_New(max_envs);
 
+    // printf("map_dir: %s, num_maps: %d\n", map_dir, num_maps);
+
     // Getting env count
     while (total_agent_count < num_agents && env_count < max_envs) {
         char map_file[512];
 
-        // Always sample randomly with replacement
+        // printf("Sampling map for env %d (total agents so far: %d)\n", env_count, total_agent_count);
+        //   Always sample randomly with replacement
         int map_id = rand() % num_maps;
 
         // printf("Sampling map_id: %d\n", map_id);
@@ -108,6 +113,7 @@ static PyObject *my_shared(PyObject *self, PyObject *args, PyObject *kwargs) {
         env->max_controlled_agents = max_controlled_agents;
         snprintf(map_file, sizeof(map_file), "%s/map_%03d.bin", map_dir, map_id);
         env->entities = load_map_binary(map_file, env);
+
         // Count the number of controllable agents in map
         set_active_agents(env);
 
@@ -198,6 +204,11 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) {
         return -1;
     }
 
+    // async_resets: default to 1 (training behavior), set 0 for eval
+    env->async_resets = conf.async_resets;
+    if (kwargs && PyDict_GetItemString(kwargs, "async_resets")) {
+        env->async_resets = (int)unpack(kwargs, "async_resets");
+    }
 // Allow all settings to be overridden via kwargs (ini provides defaults)
 #define OVERRIDE_INT(field)                                                                                            \
     if (kwargs && PyDict_GetItemString(kwargs, #field)) {                                                              \
@@ -213,8 +224,9 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) {
     OVERRIDE_INT(dynamics_model);
     OVERRIDE_FLOAT(reward_vehicle_collision);
     OVERRIDE_FLOAT(reward_offroad_collision);
+    OVERRIDE_FLOAT(obs_partner_noise_speed);
+    OVERRIDE_FLOAT(obs_partner_noise_pos);
     OVERRIDE_FLOAT(reward_goal);
-    OVERRIDE_FLOAT(reward_goal_post_respawn);
     OVERRIDE_INT(collision_behavior);
     OVERRIDE_INT(offroad_behavior);
     OVERRIDE_FLOAT(dt);
@@ -226,6 +238,10 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) {
     OVERRIDE_FLOAT(goal_radius);
     OVERRIDE_FLOAT(goal_speed);
     OVERRIDE_INT(max_controlled_agents);
+    OVERRIDE_INT(fix_rewards);
+    OVERRIDE_INT(fix_lambdas);
+    OVERRIDE_FLOAT(lambda_value);
+    OVERRIDE_INT(async_resets);
 
 #undef OVERRIDE_INT
 #undef OVERRIDE_FLOAT
@@ -234,8 +250,12 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) {
     env->dynamics_model = conf.dynamics_model;
     env->reward_vehicle_collision = conf.reward_vehicle_collision;
     env->reward_offroad_collision = conf.reward_offroad_collision;
+    env->obs_partner_noise_speed = conf.obs_partner_noise_speed;
+    env->obs_partner_noise_pos = conf.obs_partner_noise_pos;
     env->reward_goal = conf.reward_goal;
-    env->reward_goal_post_respawn = conf.reward_goal_post_respawn;
+    env->fix_rewards = conf.fix_rewards;
+    env->fix_lambdas = conf.fix_lambdas;
+    env->lambda_value = conf.lambda_value;
     env->episode_length = conf.episode_length;
     env->termination_mode = conf.termination_mode;
     env->collision_behavior = conf.collision_behavior;
@@ -259,6 +279,7 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) {
     env->map_name = strdup(map_file);
     env->init_steps = init_steps;
     env->timestep = init_steps;
+    env->async_resets = conf.async_resets;
     init(env);
     return 0;
 }
@@ -268,18 +289,18 @@ static int my_log(PyObject *dict, Log *log) {
     assign_to_dict(dict, "score", log->score);
     assign_to_dict(dict, "offroad_rate", log->offroad_rate);
     assign_to_dict(dict, "collision_rate", log->collision_rate);
+    assign_to_dict(dict, "at_fault_collision_rate", log->at_fault_collision_rate);
     assign_to_dict(dict, "episode_length", log->episode_length);
     assign_to_dict(dict, "episode_return", log->episode_return);
-    assign_to_dict(dict, "dnf_rate", log->dnf_rate);
     assign_to_dict(dict, "completion_rate", log->completion_rate);
-    assign_to_dict(dict, "lane_alignment_rate", log->lane_alignment_rate);
-    assign_to_dict(dict, "perc_controlled", log->perc_controlled);
-    assign_to_dict(dict, "perc_other", log->perc_other);
     assign_to_dict(dict, "offroad_per_agent", log->offroad_per_agent);
     assign_to_dict(dict, "collisions_per_agent", log->collisions_per_agent);
-    assign_to_dict(dict, "goals_sampled_this_episode", log->goals_sampled_this_episode);
-    assign_to_dict(dict, "goals_reached_this_episode", log->goals_reached_this_episode);
-    assign_to_dict(dict, "speed_at_goal", log->speed_at_goal);
-    // assign_to_dict(dict, "avg_displacement_error", log->avg_displacement_error);
+    assign_to_dict(dict, "perc_controlled", log->perc_controlled);
+    //  assign_to_dict(dict, "dnf_rate", log->dnf_rate);
+    // assign_to_dict(dict, "perc_other", log->perc_other);
+    // assign_to_dict(dict, "goals_sampled_this_episode", log->goals_sampled_this_episode);
+    // assign_to_dict(dict, "goals_reached_this_episode", log->goals_reached_this_episode);
+    // assign_to_dict(dict, "speed_at_goal", log->speed_at_goal);
+    //  assign_to_dict(dict, "avg_displacement_error", log->avg_displacement_error);
     return 0;
 }
diff --git a/pufferlib/ocean/drive/docs/human_regularization.md b/pufferlib/ocean/drive/docs/human_regularization.md
new file mode 100644
index 0000000000..567132a311
--- /dev/null
+++ b/pufferlib/ocean/drive/docs/human_regularization.md
@@ -0,0 +1,77 @@
+# Guided self-play from human demonstrations
+
+Everything you need to know about data-regularized self-play in your experiments.
+
+## Overview
+
+The human regularization loss constrains the learned policy to stay close to expert demonstrations by maximizing the likelihood of human actions given the corresponding human observations.
+
+## Loss Functions
+
+### Human Regularization Loss
+
+The human regularization loss is defined as the average negative log-likelihood of human actions:
+
+$$
+\mathcal{L}_{\text{human}} = -\frac{1}{B_{\text{human}}} \sum_{i=1}^{B_{\text{human}}} \log \pi_\theta(a_i^{\text{human}} \mid o_i^{\text{human}})
+$$
+
+where:
+- $B_{\text{human}}$ is the batch size of human demonstrations
+- $\pi_\theta$ is the policy being trained
+- $a_i^{\text{human}}$ is the human action for sample $i$
+- $o_i^{\text{human}}$ is the observation for sample $i$
+
+**Intuition**: This loss encourages the policy to assign high probability to actions that humans took in similar situations. By minimizing the negative log-likelihood, we maximize the probability that the policy reproduces human behavior.
+
+### Full Training Objective
+
+The complete loss combines RL objectives with human regularization:
+
+$$
+\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{PPO}} + \lambda_v \mathcal{L}_{\text{value}} - \lambda_e \mathcal{L}_{\text{entropy}} - \lambda_h \mathcal{L}_{\text{human}}
+$$
+
+where:
+- $\mathcal{L}_{\text{PPO}}$ — clipped policy gradient loss (drives RL performance)
+- $\mathcal{L}_{\text{value}}$ — value function loss (improves value estimation)
+- $\mathcal{L}_{\text{entropy}}$ — entropy bonus (encourages exploration)
+- $\lambda_h$ — weight for human regularization (controlled by `human_ll_coef`)
+
+**How regularization works**:
+- **Without regularization** ($\lambda_h = 0$): Policy optimizes purely for reward
+- **With regularization** ($\lambda_h > 0$): Policy balances maximizing reward while staying close to human demonstrations
+
+## Creating Dataset
+
+![](imgs/human-demonstrations-pipeline.png)
+
+**One-off step**. When you launch a run with `prepare_human_data=True`:
+
+1. Collect dataset of $(o, a)$ pairs (in `c_collect_expert_data()`)
+2. Process demonstrations as individual observation-action pairs
+
+> Note: Currently only works with the classic dynamics model.
+
+## Sampling Data During Training
+
+During training, we sample a batch from the human demonstration dataset:
+```python
+discrete_human_actions, continuous_human_actions, human_observations = (
+    self.vecenv.driver_env.sample_human_demonstrations()
+)
+
+human_logits, _ = self.policy(human_observations, human_state)
+
+_, human_log_prob, human_entropy = pufferlib.pytorch.sample_logits(
+    logits=human_logits, action=human_actions
+)
+```
+
+The `human_log_prob` values compute $\log \pi_\theta(a_i^{\text{human}} \mid o_i^{\text{human}})$, which are averaged and negated to get $\mathcal{L}_{\text{human}}$.
+
+## Configuration
+
+- `human_ll_coef` sets $\lambda_h$, the weight of the human regularization term
+- Typical range: 0.01 to 1.0, depending on demonstration quality
+- Set to 0 to disable regularization
diff --git a/pufferlib/ocean/drive/docs/imgs/human-demonstrations-pipeline.png b/pufferlib/ocean/drive/docs/imgs/human-demonstrations-pipeline.png
new file mode 100644
index 0000000000..4e7933821c
Binary files /dev/null and b/pufferlib/ocean/drive/docs/imgs/human-demonstrations-pipeline.png differ
diff --git a/pufferlib/ocean/drive/drive.c b/pufferlib/ocean/drive/drive.c
index 7a714ac4a1..1818ecad23 100644
--- a/pufferlib/ocean/drive/drive.c
+++ b/pufferlib/ocean/drive/drive.c
@@ -35,8 +35,7 @@ void test_drivenet() {
 }
 
 int demo(const char *map_name, const char *policy_name, int show_grid, int obs_only, int lasers, int show_human_logs,
-         int frame_skip, const char *view_mode, const char *output_topdown, const char *output_agent, int num_maps,
-         int zoom_in) {
+         const char *view_mode, const char *output_topdown, const char *output_agent, int num_maps, int zoom_in) {
 
     // Parse configuration from INI file
     env_init_config conf = {0};
@@ -61,7 +60,6 @@ int demo(const char *map_name, const char *policy_name, int show_grid, int obs_o
         .reward_vehicle_collision = conf.reward_vehicle_collision,
         .reward_offroad_collision = conf.reward_offroad_collision,
         .reward_goal = conf.reward_goal,
-        .reward_goal_post_respawn = conf.reward_goal_post_respawn,
         .goal_radius = conf.goal_radius,
         .goal_behavior = conf.goal_behavior,
         .goal_target_distance = conf.goal_target_distance,
@@ -208,7 +206,6 @@ int main(int argc, char *argv[]) {
     int obs_only = 0;
     int lasers = 0;
     int show_human_logs = 0;
-    int frame_skip = 1;
     int zoom_in = 0;
     const char *view_mode = "both";
 
@@ -229,14 +226,6 @@ int main(int argc, char *argv[]) {
             lasers = 1;
         } else if (strcmp(argv[i], "--log-trajectories") == 0) {
             show_human_logs = 1;
-        } else if (strcmp(argv[i], "--frame-skip") == 0) {
-            if (i + 1 < argc) {
-                frame_skip = atoi(argv[i + 1]);
-                i++;
-                if (frame_skip <= 0) {
-                    frame_skip = 1;
-                }
-            }
         } else if (strcmp(argv[i], "--zoom-in") == 0) {
             zoom_in = 1;
         } else if (strcmp(argv[i], "--view") == 0) {
@@ -287,8 +276,8 @@ int main(int argc, char *argv[]) {
     }
 
     // performance_test();
-    demo(map_name, policy_name, show_grid, obs_only, lasers, show_human_logs, frame_skip, view_mode, output_topdown,
-         output_agent, num_maps, zoom_in);
+    demo(map_name, policy_name, show_grid, obs_only, lasers, show_human_logs, view_mode, output_topdown, output_agent,
+         num_maps, zoom_in);
     // test_drivenet();
     return 0;
 }
diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h
index 2a55867120..cc64565bab 100644
--- a/pufferlib/ocean/drive/drive.h
+++ b/pufferlib/ocean/drive/drive.h
@@ -22,11 +22,12 @@
 #define VIEW_MODE_SIM_STATE 0
 #define VIEW_MODE_BEV_AGENT_OBS 1
 #define VIEW_MODE_AGENT_PERSP 2
+#define VIEW_MODE_SENSOR_NOISE 3
 
 // Order of entities in rendering (lower is rendered first)
 #define Z_ROAD_SURFACE 0.0f
 #define Z_ROAD_MARKINGS 0.05f // Lane lines, road lines, traces
-#define Z_AGENT_DETAILS 0.4f  // Arrow, goal markers, obs overlays
+#define Z_AGENT_DETAILS 0.2f  // Arrow, goal markers, obs overlays
 #define Z_AGENTS 0.6f         // Vehicles, cyclists, pedestrians
 
 // Entity Types
@@ -57,33 +58,24 @@
 #define CONTROL_WOSAC 2
 #define CONTROL_SDC_ONLY 3
 #define CONTROL_MIXED_PLAY 4
+#define CONTROL_INFERRED_EXPERT_ACTIONS 5
+#define CONTROL_REPLAY_LOGS 6
 
 // Minimum distance to goal position
 #define MIN_DISTANCE_TO_GOAL 2.0f
 
-// Actions
-#define NOOP 0
+// Threshold for data filtering
+#define ADE_THRESHOLD 2.0f
 
-// Dynamics Models
+// Dynamics models
 #define CLASSIC 0
 #define JERK 1
-
-// Collision state
-#define NO_COLLISION 0
-#define VEHICLE_COLLISION 1
-#define OFFROAD 2
-
-// Metrics array indices
-#define COLLISION_IDX 0
-#define OFFROAD_IDX 1
-#define REACHED_GOAL_IDX 2
-#define LANE_ALIGNED_IDX 3
-
-// Grid cell size
+#define DELTA_LOCAL 2
+// Grid cell size: Depends on resolution of data
+// Formula: 3 * (2 + GRID_CELL_SIZE*sqrt(2)/resolution) => For each entity type in
+// gridmap, diagonal poly-lines -> sqrt(2), include diagonal ends -> 2
 #define GRID_CELL_SIZE 5.0f
-#define MAX_ENTITIES_PER_CELL                                                                                          \
-    30 // Depends on resolution of data Formula: 3 * (2 + GRID_CELL_SIZE*sqrt(2)/resolution) => For each entity type in
-       // gridmap, diagonal poly-lines -> sqrt(2), include diagonal ends -> 2
+#define MAX_ENTITIES_PER_CELL 30
 
 // Observation constants
 #define MAX_ROAD_SEGMENT_OBSERVATIONS 128
@@ -100,8 +92,13 @@
 #define PARTNER_FEATURES 7
 
 // Ego features depend on dynamics model
-#define EGO_FEATURES_CLASSIC 8
-#define EGO_FEATURES_JERK 11
+#define EGO_FEATURES 12
+#define EGO_FEATURES_JERK 15
+
+#define LAMBDA_CONDITIONING_IDX 0
+#define REWARD_COLLISION_IDX 1
+#define REWARD_OFFROAD_COLLISION_IDX 2
+#define REWARD_GOAL_IDX 3
 
 // Observation normalization constants
 #define MAX_SPEED 100.0f
@@ -119,7 +116,7 @@
 #define MAX_ROAD_SEGMENT_LENGTH 100.0f
 
 // Goal behavior
-#define GOAL_RESPAWN 0
+#define GOAL_REMOVE 0
 #define GOAL_GENERATE_NEW 1
 #define GOAL_STOP 2
 
@@ -128,9 +125,26 @@ static const float JERK_LONG[4] = {-15.0f, -4.0f, 0.0f, 4.0f};
 static const float JERK_LAT[3] = {-4.0f, 0.0f, 4.0f};
 
 // Classic action space (for CLASSIC dynamics model)
-static const float ACCELERATION_VALUES[7] = {-4.0000f, -2.6670f, -1.3330f, -0.0000f, 1.3330f, 2.6670f, 4.0000f};
-static const float STEERING_VALUES[13] = {-1.000f, -0.833f, -0.667f, -0.500f, -0.333f, -0.167f, 0.000f,
-                                          0.167f,  0.333f,  0.500f,  0.667f,  0.833f,  1.000f};
+#define NUM_ACCEL_BINS 21
+#define ACCEL_MIN -4.0f
+#define ACCEL_MAX 4.0f
+
+#define NUM_STEER_BINS 31
+#define STEER_MIN -1.0f // radians
+#define STEER_MAX 1.0f
+#define NUM_DX_BINS 31
+#define NUM_DY_BINS 31
+#define NUM_YAW_BINS 127
+#define DELTA_MAX_DX 2.5f
+#define DELTA_MAX_DY 2.0f
+#define DELTA_MAX_DYAW 3.14159265 / 4.0
+
+static int COLLECT_EXPERT_TELEPORT = 1;
+static float ACCELERATION_VALUES[NUM_ACCEL_BINS];
+static float STEERING_VALUES[NUM_STEER_BINS];
+static float DELTA_DX_VALUES[NUM_DX_BINS];
+static float DELTA_DY_VALUES[NUM_DY_BINS];
+static float DELTA_YAW_VALUES[NUM_YAW_BINS];
 
 static const float offsets[4][2] = {
     {-1, 1}, // top-left
@@ -147,6 +161,8 @@ static const int collision_offsets[25][2] = {
     {-2, 2},  {-1, 2},  {0, 2},  {1, 2},  {2, 2}   // Bottom row
 };
 
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+
 const Color STONE_GRAY = (Color){80, 80, 80, 255};
 const Color PUFF_RED = (Color){187, 0, 0, 255};
 const Color PUFF_CYAN = (Color){0, 187, 187, 255};
@@ -164,8 +180,6 @@ const Color EXPERT_REPLAY_SMALL = (Color){95, 112, 93, 255};
 const Color LIGHT_ORANGE = (Color){255, 160, 80, 255};
 const Color LIGHT_PURPLE = (Color){204, 204, 255, 255};
 
-struct timespec ts;
-
 typedef struct Drive Drive;
 typedef struct Client Client;
 typedef struct Log Log;
@@ -178,18 +192,18 @@ struct Log {
     float goals_sampled_this_episode;
     float offroad_rate;
     float collision_rate;
+    float at_fault_collision_rate;
     float completion_rate;
     float offroad_per_agent;
     float collisions_per_agent;
     float dnf_rate;
-    float n;
-    float lane_alignment_rate;
     float speed_at_goal;
     float active_agent_count;
     float expert_static_agent_count;
     float static_agent_count;
     float perc_controlled;
     float perc_other;
+    float n;
 };
 
 typedef struct Entity Entity;
@@ -206,6 +220,14 @@ struct Entity {
     float *traj_vz;
     float *traj_heading;
     int *traj_valid;
+    float *expert_accel;
+    float *expert_steering;
+    float *expert_delta_x;
+    float *expert_delta_y;
+    float *expert_delta_yaw;
+    float *inferred_traj_x;
+    float *inferred_traj_y;
+    float inferred_ade;
     float width;
     float length;
     float height;
@@ -216,7 +238,8 @@ struct Entity {
     float init_goal_y;
     int mark_as_expert;
     int collision_state;
-    float metrics_array[5]; // metrics_array: [collision, offroad, reached_goal, lane_aligned
+    int at_fault_collision_state;
+    int offroad_state;
     float x;
     float y;
     float z;
@@ -226,25 +249,26 @@ struct Entity {
     float heading;
     float heading_x;
     float heading_y;
-    int current_lane_idx;
     int valid;
-    int respawn_timestep;
-    int respawn_count;
-    int collided_before_goal;
+    int failure_before_goal;
     float goals_reached_this_episode;
     float goals_sampled_this_episode;
     int current_goal_reached;
     int active_agent;
     int stopped;
     int removed;
-
-    // Jerk dynamics
     float a_long;
     float a_lat;
     float jerk_long;
     float jerk_lat;
     float steering_angle;
     float wheelbase;
+    // Lambda conditioning (human regularization)
+    float lambda;
+    // Reward conditioning
+    float reward_collision_cond;
+    float reward_offroad_cond;
+    float reward_goal_cond;
 };
 
 void free_entity(Entity *entity) {
@@ -257,12 +281,22 @@ void free_entity(Entity *entity) {
     free(entity->traj_vz);
     free(entity->traj_heading);
     free(entity->traj_valid);
+    free(entity->inferred_traj_x);
+    free(entity->inferred_traj_y);
+    free(entity->expert_accel);
+    free(entity->expert_steering);
+    free(entity->expert_delta_x);
+    free(entity->expert_delta_y);
+    free(entity->expert_delta_yaw);
 }
 
 // Utility functions
-float relative_distance(float a, float b) {
-    float distance = sqrtf(powf(a - b, 2));
-    return distance;
+static float gaussian_noise(float sigma) {
+    if (sigma <= 0.0f)
+        return 0.0f;
+    float u1 = ((float)rand() + 1.0f) / ((float)RAND_MAX + 1.0f);
+    float u2 = ((float)rand() + 1.0f) / ((float)RAND_MAX + 1.0f);
+    return sigma * sqrtf(-2.0f * logf(u1)) * cosf(2.0f * M_PI * u2);
 }
 
 float relative_distance_2d(float x1, float y1, float x2, float y2) {
@@ -334,14 +368,16 @@ struct Drive {
     int *neighbor_offsets;
     int episode_length;
     int termination_mode;
-    float reward_vehicle_collision;
-    float reward_offroad_collision;
     char *map_name;
     float world_mean_x;
     float world_mean_y;
     float dt;
+    int fix_rewards;
     float reward_goal;
-    float reward_goal_post_respawn;
+    float reward_vehicle_collision;
+    float reward_offroad_collision;
+    int fix_lambdas;
+    float lambda_value;
     float goal_radius;
     float goal_speed;
     int logs_capacity;
@@ -358,6 +394,13 @@ struct Drive {
     int control_mode;
     int max_controlled_agents;
     int render_mode;
+    // Noise configuration
+    float dynamics_noise_pos;
+    float dynamics_noise_heading;
+    float obs_partner_noise_pos;
+    float obs_partner_noise_speed;
+    float obs_noise_road;
+    bool async_resets;
 };
 
 void add_log(Drive *env) {
@@ -366,15 +409,11 @@ void add_log(Drive *env) {
 
         env->log.goals_reached_this_episode += e->goals_reached_this_episode;
         env->log.goals_sampled_this_episode += e->goals_sampled_this_episode;
-
-        int offroad = env->logs[i].offroad_rate;
-        env->log.offroad_rate += offroad;
-        int collided = env->logs[i].collision_rate;
-        env->log.collision_rate += collided;
-        float offroad_per_agent = env->logs[i].offroad_per_agent;
-        env->log.offroad_per_agent += offroad_per_agent;
-        float collisions_per_agent = env->logs[i].collisions_per_agent;
-        env->log.collisions_per_agent += collisions_per_agent;
+        env->log.offroad_rate += env->logs[i].offroad_rate;
+        env->log.collision_rate += env->logs[i].collision_rate;
+        env->log.offroad_per_agent += env->logs[i].offroad_per_agent;
+        env->log.collisions_per_agent += env->logs[i].collisions_per_agent;
+        env->log.at_fault_collision_rate += env->logs[i].at_fault_collision_rate;
 
         float frac_goal_reached = e->goals_reached_this_episode / e->goals_sampled_this_episode;
 
@@ -385,19 +424,16 @@ void add_log(Drive *env) {
             threshold = (e->goals_sampled_this_episode - 1.0f) / e->goals_sampled_this_episode;
         }
 
-        int collision_occurred =
-            (env->goal_behavior == GOAL_RESPAWN) ? e->collided_before_goal : env->logs[i].collision_rate;
-        if (frac_goal_reached >= threshold && !collision_occurred) {
+        if (frac_goal_reached >= threshold && !e->failure_before_goal) {
             env->log.score += 1.0f;
         }
-        if (!offroad && !collided && frac_goal_reached < 1.0f) {
+        if (!e->failure_before_goal && frac_goal_reached < 1.0f) {
             env->log.dnf_rate += 1.0f;
         }
-        int lane_aligned = env->logs[i].lane_alignment_rate;
-        env->log.lane_alignment_rate += lane_aligned;
         env->log.speed_at_goal += env->logs[i].speed_at_goal;
         env->log.episode_length += env->logs[i].episode_length;
         env->log.episode_return += env->logs[i].episode_return;
+
         // Log composition counts per agent so vec_log averaging recovers the per-env value
         env->log.active_agent_count += env->active_agent_count;
         env->log.expert_static_agent_count += env->expert_static_agent_count;
@@ -409,6 +445,32 @@ void add_log(Drive *env) {
     }
 }
 
+void init_action_space() {
+    // Classic discrete action space
+    float accel_step = (ACCEL_MAX - ACCEL_MIN) / (NUM_ACCEL_BINS - 1);
+    for (int i = 0; i < NUM_ACCEL_BINS; i++) {
+        ACCELERATION_VALUES[i] = ACCEL_MIN + i * accel_step;
+    }
+
+    float steer_step = (STEER_MAX - STEER_MIN) / (NUM_STEER_BINS - 1);
+    for (int i = 0; i < NUM_STEER_BINS; i++) {
+        STEERING_VALUES[i] = STEER_MIN + i * steer_step;
+    }
+    // Delta-local discrete action space
+    float dx_step = (2.0f * DELTA_MAX_DX) / (NUM_DX_BINS - 1);
+    for (int i = 0; i < NUM_DX_BINS; i++) {
+        DELTA_DX_VALUES[i] = -DELTA_MAX_DX + i * dx_step;
+    }
+    float dy_step = (2.0f * DELTA_MAX_DY) / (NUM_DY_BINS - 1);
+    for (int i = 0; i < NUM_DY_BINS; i++) {
+        DELTA_DY_VALUES[i] = -DELTA_MAX_DY + i * dy_step;
+    }
+    float yaw_step = (2.0f * DELTA_MAX_DYAW) / (NUM_YAW_BINS - 1);
+    for (int i = 0; i < NUM_YAW_BINS; i++) {
+        DELTA_YAW_VALUES[i] = -DELTA_MAX_DYAW + i * yaw_step;
+    }
+}
+
 Entity *load_map_binary(const char *filename, Drive *env) {
     FILE *file = fopen(filename, "rb");
     if (!file)
@@ -436,6 +498,7 @@ Entity *load_map_binary(const char *filename, Drive *env) {
     fread(&env->num_roads, sizeof(int), 1, file);
     env->num_entities = env->num_objects + env->num_roads;
     Entity *entities = (Entity *)malloc(env->num_entities * sizeof(Entity));
+
     for (int i = 0; i < env->num_entities; i++) {
         // Read base entity data
         fread(&entities[i].scenario_id, sizeof(int), 1, file);
@@ -455,6 +518,13 @@ Entity *load_map_binary(const char *filename, Drive *env) {
             entities[i].traj_vz = (float *)malloc(size * sizeof(float));
             entities[i].traj_heading = (float *)malloc(size * sizeof(float));
             entities[i].traj_valid = (int *)malloc(size * sizeof(int));
+            entities[i].inferred_traj_x = (float *)calloc(size, sizeof(float));
+            entities[i].inferred_traj_y = (float *)calloc(size, sizeof(float));
+            entities[i].expert_accel = (float *)malloc(size * sizeof(float));
+            entities[i].expert_steering = (float *)malloc(size * sizeof(float));
+            entities[i].expert_delta_x = (float *)malloc(size * sizeof(float));
+            entities[i].expert_delta_y = (float *)malloc(size * sizeof(float));
+            entities[i].expert_delta_yaw = (float *)malloc(size * sizeof(float));
         } else {
             // Roads don't use these arrays
             entities[i].traj_vx = NULL;
@@ -462,6 +532,13 @@ Entity *load_map_binary(const char *filename, Drive *env) {
             entities[i].traj_vz = NULL;
             entities[i].traj_heading = NULL;
             entities[i].traj_valid = NULL;
+            entities[i].inferred_traj_x = NULL;
+            entities[i].inferred_traj_y = NULL;
+            entities[i].expert_accel = NULL;
+            entities[i].expert_steering = NULL;
+            entities[i].expert_delta_x = NULL;
+            entities[i].expert_delta_y = NULL;
+            entities[i].expert_delta_yaw = NULL;
         }
         // Read array data
         fread(entities[i].traj_x, sizeof(float), size, file);
@@ -474,6 +551,11 @@ Entity *load_map_binary(const char *filename, Drive *env) {
             fread(entities[i].traj_vz, sizeof(float), size, file);
             fread(entities[i].traj_heading, sizeof(float), size, file);
             fread(entities[i].traj_valid, sizeof(int), size, file);
+            fread(entities[i].expert_accel, sizeof(float), size, file);
+            fread(entities[i].expert_steering, sizeof(float), size, file);
+            fread(entities[i].expert_delta_x, sizeof(float), size, file);
+            fread(entities[i].expert_delta_y, sizeof(float), size, file);
+            fread(entities[i].expert_delta_yaw, sizeof(float), size, file);
         }
         // Read remaining scalar fields
         fread(&entities[i].width, sizeof(float), 1, file);
@@ -517,25 +599,21 @@ void set_start_position(Drive *env) {
             e->vx = 0;
             e->vy = 0;
             e->vz = 0;
-            e->collided_before_goal = 0;
+            e->failure_before_goal = 0;
         } else {
-            e->vx = e->traj_vx[env->init_steps];
-            e->vy = e->traj_vy[env->init_steps];
-            e->vz = e->traj_vz[env->init_steps];
+            e->vx = e->traj_vx[step];
+            e->vy = e->traj_vy[step];
+            e->vz = e->traj_vz[step];
         }
-        e->heading = e->traj_heading[env->init_steps];
+        e->heading = e->traj_heading[step];
         e->heading_x = cosf(e->heading);
         e->heading_y = sinf(e->heading);
-        e->valid = e->traj_valid[env->init_steps];
+        e->valid = e->traj_valid[step];
         e->collision_state = 0;
-        e->metrics_array[COLLISION_IDX] = 0.0f;    // vehicle collision
-        e->metrics_array[OFFROAD_IDX] = 0.0f;      // offroad
-        e->metrics_array[REACHED_GOAL_IDX] = 0.0f; // reached goal
-        e->metrics_array[LANE_ALIGNED_IDX] = 0.0f; // lane aligned
-        e->respawn_timestep = -1;
+        e->at_fault_collision_state = 0;
+        e->offroad_state = 0;
         e->stopped = 0;
         e->removed = 0;
-        e->respawn_count = 0;
 
         // Dynamics
         e->a_long = 0.0f;
@@ -543,7 +621,7 @@ void set_start_position(Drive *env) {
         e->jerk_long = 0.0f;
         e->jerk_lat = 0.0f;
         e->steering_angle = 0.0f;
-        e->wheelbase = 0.6f * e->length;
+        e->wheelbase = e->length;
     }
 }
 
@@ -859,6 +937,16 @@ void move_expert(Drive *env, float *actions, int agent_idx) {
     agent->heading = agent->traj_heading[t];
     agent->heading_x = cosf(agent->heading);
     agent->heading_y = sinf(agent->heading);
+
+    if (agent->traj_vx != NULL) {
+        agent->vx = agent->traj_vx[t];
+        agent->vy = agent->traj_vy[t];
+        agent->vz = agent->traj_vz[t];
+    } else if (t > 0 && env->dt > 0.0f) {
+        agent->vx = (agent->traj_x[t] - agent->traj_x[t - 1]) / env->dt;
+        agent->vy = (agent->traj_y[t] - agent->traj_y[t - 1]) / env->dt;
+        agent->vz = (agent->traj_z[t] - agent->traj_z[t - 1]) / env->dt;
+    }
 }
 
 bool check_line_intersection(float p1[2], float p2[2], float q1[2], float q2[2]) {
@@ -1000,8 +1088,8 @@ int collision_check(Drive *env, int agent_idx) {
 
     int car_collided_with_index = -1;
 
-    if (agent->respawn_timestep != -1)
-        return car_collided_with_index; // Skip respawning entities
+    if (agent->removed == 1)
+        return car_collided_with_index; // Skip removed entities
 
     for (int i = 0; i < MAX_AGENTS; i++) {
         int index = -1;
@@ -1015,8 +1103,8 @@ int collision_check(Drive *env, int agent_idx) {
         if (index == agent_idx)
             continue;
         Entity *entity = &env->entities[index];
-        if (entity->respawn_timestep != -1)
-            continue; // Skip respawning entities
+        if (entity->removed == 1)
+            continue; // Skip removed entities
         float x1 = entity->x;
         float y1 = entity->y;
         float dist = ((x1 - agent->x) * (x1 - agent->x) + (y1 - agent->y) * (y1 - agent->y));
@@ -1031,104 +1119,20 @@ int collision_check(Drive *env, int agent_idx) {
     return car_collided_with_index;
 }
 
-int check_lane_aligned(Entity *car, Entity *lane, int geometry_idx) {
-    // Validate lane geometry length
-    if (!lane || lane->array_size < 2)
-        return 0;
-
-    // Clamp geometry index to valid segment range [0, array_size-2]
-    if (geometry_idx < 0)
-        geometry_idx = 0;
-    if (geometry_idx >= lane->array_size - 1)
-        geometry_idx = lane->array_size - 2;
-
-    // Compute local lane segment heading
-    float heading_x1, heading_y1;
-    if (geometry_idx > 0) {
-        heading_x1 = lane->traj_x[geometry_idx] - lane->traj_x[geometry_idx - 1];
-        heading_y1 = lane->traj_y[geometry_idx] - lane->traj_y[geometry_idx - 1];
-    } else {
-        // For first segment, just use the forward direction
-        heading_x1 = lane->traj_x[geometry_idx + 1] - lane->traj_x[geometry_idx];
-        heading_y1 = lane->traj_y[geometry_idx + 1] - lane->traj_y[geometry_idx];
-    }
-
-    float heading_x2 = lane->traj_x[geometry_idx + 1] - lane->traj_x[geometry_idx];
-    float heading_y2 = lane->traj_y[geometry_idx + 1] - lane->traj_y[geometry_idx];
-
-    float heading_1 = atan2f(heading_y1, heading_x1);
-    float heading_2 = atan2f(heading_y2, heading_x2);
-    float heading = (heading_1 + heading_2) / 2.0f;
-
-    // Normalize to [-pi, pi]
-    if (heading > M_PI)
-        heading -= 2.0f * M_PI;
-    if (heading < -M_PI)
-        heading += 2.0f * M_PI;
-
-    // Compute heading difference
-    float car_heading = car->heading; // radians
-    float heading_diff = fabsf(car_heading - heading);
-
-    if (heading_diff > M_PI)
-        heading_diff = 2.0f * M_PI - heading_diff;
-
-    // within 15 degrees
-    return (heading_diff < (M_PI / 12.0f)) ? 1 : 0;
-}
-
-void reset_agent_metrics(Drive *env, int agent_idx) {
-    Entity *agent = &env->entities[agent_idx];
-    agent->metrics_array[COLLISION_IDX] = 0.0f;    // vehicle collision
-    agent->metrics_array[OFFROAD_IDX] = 0.0f;      // offroad
-    agent->metrics_array[LANE_ALIGNED_IDX] = 0.0f; // lane aligned
-    agent->collision_state = 0;
-}
-
-float point_to_segment_distance_2d(float px, float py, float x1, float y1, float x2, float y2) {
-    float dx = x2 - x1;
-    float dy = y2 - y1;
-
-    if (dx == 0 && dy == 0) {
-        // The segment is a point
-        return sqrtf((px - x1) * (px - x1) + (py - y1) * (py - y1));
-    }
-
-    // Calculate the t that minimizes the distance
-    float t = ((px - x1) * dx + (py - y1) * dy) / (dx * dx + dy * dy);
-
-    // Clamp t to the segment
-    if (t < 0)
-        t = 0;
-    else if (t > 1)
-        t = 1;
-
-    // Find the closest point on the segment
-    float closestX = x1 + t * dx;
-    float closestY = y1 + t * dy;
-
-    // Return the distance from p to the closest point
-    return sqrtf((px - closestX) * (px - closestX) + (py - closestY) * (py - closestY));
-}
-
 void compute_agent_metrics(Drive *env, int agent_idx) {
     Entity *agent = &env->entities[agent_idx];
 
-    reset_agent_metrics(env, agent_idx);
-
     if (agent->x == INVALID_POSITION)
         return; // invalid agent position
 
-    int collided = 0;
+    int collided_with_agent = 0;
+    int collided_with_road = 0;
+
+    // Get agent bb measurements
     float half_length = agent->length / 2.0f;
     float half_width = agent->width / 2.0f;
     float cos_heading = cosf(agent->heading);
     float sin_heading = sinf(agent->heading);
-    float min_distance = (float)INT16_MAX;
-
-    int closest_lane_entity_idx = -1;
-    int closest_lane_geometry_idx = -1;
-
     float corners[4][2];
     for (int i = 0; i < 4; i++) {
         corners[i][0] =
@@ -1140,6 +1144,8 @@ void compute_agent_metrics(Drive *env, int agent_idx) {
     GridMapEntity entity_list[MAX_ENTITIES_PER_CELL * 25]; // Array big enough for all neighboring cells
     int list_size =
         checkNeighbors(env, agent->x, agent->y, entity_list, MAX_ENTITIES_PER_CELL * 25, collision_offsets, 25);
+
+    // Iterate through road entities and check for off-road event
     for (int i = 0; i < list_size; i++) {
         if (entity_list[i].entity_idx == -1)
             continue;
@@ -1156,65 +1162,39 @@ void compute_agent_metrics(Drive *env, int agent_idx) {
             for (int k = 0; k < 4; k++) { // Check each edge of the bounding box
                 int next = (k + 1) % 4;
                 if (check_line_intersection(corners[k], corners[next], start, end)) {
-                    collided = OFFROAD;
+                    collided_with_road = 1;
                     break;
                 }
             }
         }
 
-        if (collided == OFFROAD)
+        if (collided_with_road == 1)
             break;
-
-        // Find closest point on the road centerline to the agent
-        if (entity->type == ROAD_LANE) {
-            int entity_idx = entity_list[i].entity_idx;
-            int geometry_idx = entity_list[i].geometry_idx;
-
-            float start[2] = {entity->traj_x[geometry_idx], entity->traj_y[geometry_idx]};
-            float end[2] = {entity->traj_x[geometry_idx + 1], entity->traj_y[geometry_idx + 1]};
-
-            float dist = point_to_segment_distance_2d(agent->x, agent->y, start[0], start[1], end[0], end[1]);
-            float heading_diff = fabsf(atan2f(end[1] - start[1], end[0] - start[0]) - agent->heading);
-
-            // Normalize heading difference to [0, pi]
-            if (heading_diff > M_PI)
-                heading_diff = 2.0f * M_PI - heading_diff;
-
-            // Penalize if heading differs by more than 30 degrees
-            if (heading_diff > (M_PI / 6.0f))
-                dist += 3.0f;
-
-            if (dist < min_distance) {
-                min_distance = dist;
-                closest_lane_entity_idx = entity_idx;
-                closest_lane_geometry_idx = geometry_idx;
-            }
-        }
-    }
-
-    // check if aligned with closest lane and set current lane
-    // 4.0m threshold: agents more than 4 meters from any lane are considered off-road
-    if (min_distance > 4.0f || closest_lane_entity_idx == -1) {
-        agent->metrics_array[LANE_ALIGNED_IDX] = 0.0f;
-        agent->current_lane_idx = -1;
-    } else {
-        agent->current_lane_idx = closest_lane_entity_idx;
-        int lane_aligned =
-            check_lane_aligned(agent, &env->entities[closest_lane_entity_idx], closest_lane_geometry_idx);
-        agent->metrics_array[LANE_ALIGNED_IDX] = lane_aligned;
     }
-
-    // Check for vehicle collisions (skip for pedestrians)
+    // Check for collisions with other agents (skip for pedestrians)
     int car_collided_with_index = -1;
     if (agent->type != PEDESTRIAN) {
         car_collided_with_index = collision_check(env, agent_idx);
-        if (car_collided_with_index != -1)
-            collided = VEHICLE_COLLISION;
+        if (car_collided_with_index != -1) {
+            collided_with_agent = 1;
+            agent->collision_state = 1;
+        }
     }
 
-    agent->collision_state = collided;
+    if (collided_with_agent) {
+        // Determine fault: was this agent moving toward the other?
+        Entity *other = &env->entities[car_collided_with_index];
 
-    if (collided == VEHICLE_COLLISION) {
+        // Vector from this agent to the other
+        float dx = other->x - agent->x;
+        float dy = other->y - agent->y;
+
+        float forward_dot = dx * agent->heading_x + dy * agent->heading_y;
+        float approach_dot = agent->vx * dx + agent->vy * dy;
+
+        if (forward_dot > 0 && approach_dot > 0) {
+            agent->at_fault_collision_state = 1;
+        }
         if (env->collision_behavior == STOP_AGENT && !agent->stopped) {
             agent->stopped = 1;
             agent->vx = agent->vy = 0.0f;
@@ -1226,8 +1206,8 @@ void compute_agent_metrics(Drive *env, int agent_idx) {
             agent_collided->x = agent_collided->y = -10000.0f;
         }
     }
-    if (collided == OFFROAD) {
-        agent->metrics_array[OFFROAD_IDX] = 1.0f;
+    if (collided_with_road) {
+        agent->offroad_state = 1;
         if (env->offroad_behavior == STOP_AGENT && !agent->stopped) {
             agent->stopped = 1;
             agent->vx = agent->vy = 0.0f;
@@ -1258,6 +1238,8 @@ bool should_control_agent(Drive *env, int agent_idx, int control_limit) {
 
     switch (env->control_mode) {
     case CONTROL_WOSAC:
+    case CONTROL_INFERRED_EXPERT_ACTIONS:
+    case CONTROL_REPLAY_LOGS:
         // Valid types only, ignore expert flag and goal distance
         return (is_vehicle || is_ped_or_bike);
 
@@ -1307,7 +1289,7 @@ void set_active_agents(Drive *env) {
 
     int control_limit;
     if (env->control_mode == CONTROL_MIXED_PLAY) {
-        control_limit = (env->max_controlled_agents < env->num_agents) ? env->max_controlled_agents : env->num_agents;
+        control_limit = MIN(env->max_controlled_agents, env->num_agents);
     } else {
         control_limit = env->num_agents;
     }
@@ -1365,7 +1347,7 @@ void set_active_agents(Drive *env) {
             static_agent_indices[env->static_agent_count] = i;
             env->static_agent_count++; // Includes expert replay and static agents
             env->entities[i].active_agent = 0;
-            if (env->entities[i].mark_as_expert == 1 || env->active_agent_count == control_limit) {
+            if (env->entities[i].mark_as_expert == 1 || env->active_agent_count >= control_limit) {
                 expert_static_agent_indices[env->expert_static_agent_count] = i;
                 env->expert_static_agent_count++;
                 env->entities[i].mark_as_expert = 1;
@@ -1395,8 +1377,9 @@ void set_active_agents(Drive *env) {
 
 void remove_bad_trajectories(Drive *env) {
 
-    if (env->control_mode != CONTROL_WOSAC) {
-        return; // Leave all trajectories in WOSAC control mode
+    if (env->control_mode != CONTROL_WOSAC && env->control_mode != CONTROL_INFERRED_EXPERT_ACTIONS &&
+        env->control_mode != CONTROL_REPLAY_LOGS) {
+        return;
     }
 
     set_start_position(env);
@@ -1445,17 +1428,10 @@ void remove_bad_trajectories(Drive *env) {
     env->timestep = 0;
 }
 
-void init_goal_positions(Drive *env) {
-    for (int x = 0; x < env->active_agent_count; x++) {
-        int agent_idx = env->active_agent_indices[x];
-        env->entities[agent_idx].init_goal_x = env->entities[agent_idx].goal_position_x;
-        env->entities[agent_idx].init_goal_y = env->entities[agent_idx].goal_position_y;
-    }
-}
-
 void init(Drive *env) {
     env->human_agent_idx = 0;
     env->timestep = 0;
+    init_action_space();
     env->entities = load_map_binary(env->map_name, env);
     set_means(env);
     init_grid_map(env);
@@ -1467,15 +1443,30 @@ void init(Drive *env) {
     env->logs_capacity = env->active_agent_count;
     remove_bad_trajectories(env);
     set_start_position(env);
-    init_goal_positions(env);
+
+    for (int x = 0; x < env->active_agent_count; x++) {
+        int agent_idx = env->active_agent_indices[x];
+        env->entities[agent_idx].init_goal_x = env->entities[agent_idx].goal_position_x;
+        env->entities[agent_idx].init_goal_y = env->entities[agent_idx].goal_position_y;
+    }
+
     env->logs = (Log *)calloc(env->active_agent_count, sizeof(Log));
+    env->dynamics_noise_pos = 0.0f; // 0.0125f; Gigaflow
+    env->dynamics_noise_heading = 0.0f;
+    env->obs_noise_road = 0.0f;
+    if (env->async_resets != 0 && env->async_resets != 1) {
+        env->async_resets = 1; // Default to async resets
+    }
 }
 
 void close_client(Client *client);
 
 void c_close(Drive *env) {
     if (env->client != NULL) {
-        close_client(env->client);
+        stop_recorder(env->client);
+        if (env->render_mode != RENDER_HEADLESS) {
+            close_client(env->client);
+        }
         env->client = NULL;
     }
     for (int i = 0; i < env->num_entities; i++) {
@@ -1508,10 +1499,20 @@ void c_close(Drive *env) {
 
 void allocate(Drive *env) {
     init(env);
-    int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC;
+    int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES;
     int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS;
     env->observations = (float *)calloc(env->active_agent_count * max_obs, sizeof(float));
-    env->actions = (float *)calloc(env->active_agent_count * 2, sizeof(float));
+
+    // All discrete models use a single int per agent (cast to float storage)
+    // All continuous models use action_dim floats per agent
+    if (env->action_type == 1) { // continuous
+        int action_dim = (env->dynamics_model == DELTA_LOCAL) ? 3 : 2;
+        env->actions = (float *)calloc(env->active_agent_count * action_dim, sizeof(float));
+    } else { // discrete
+        int actions_per_agent = (env->dynamics_model == DELTA_LOCAL) ? 3 : 1;
+        env->actions = (float *)calloc(env->active_agent_count * actions_per_agent, sizeof(int));
+    }
+
     env->rewards = (float *)calloc(env->active_agent_count, sizeof(float));
     env->terminals = (unsigned char *)calloc(env->active_agent_count, sizeof(unsigned char));
     env->truncations = (unsigned char *)calloc(env->active_agent_count, sizeof(unsigned char));
@@ -1543,8 +1544,6 @@ float normalize_heading(float heading) {
     return heading;
 }
 
-float normalize_value(float value, float min, float max) { return (value - min) / (max - min); }
-
 void move_dynamics(Drive *env, int action_idx, int agent_idx) {
     Entity *agent = &env->entities[agent_idx];
     if (agent->removed)
@@ -1566,12 +1565,12 @@ void move_dynamics(Drive *env, int action_idx, int agent_idx) {
             acceleration = action_array_f[action_idx][0];
             steering = action_array_f[action_idx][1];
 
-            acceleration *= ACCELERATION_VALUES[6];
-            steering *= STEERING_VALUES[12];
+            acceleration *= ACCEL_MAX;
+            steering *= STEER_MAX;
         } else { // discrete
             // Interpret action as a single integer: a = accel_idx * num_steer + steer_idx
             int *action_array = (int *)env->actions;
-            int num_steer = sizeof(STEERING_VALUES) / sizeof(STEERING_VALUES[0]);
+            int num_steer = NUM_STEER_BINS;
             int action_val = action_array[action_idx];
             int acceleration_index = action_val / num_steer;
             int steering_index = action_val % num_steer;
@@ -1598,6 +1597,8 @@ void move_dynamics(Drive *env, int action_idx, int agent_idx) {
         float beta = tanh(.5 * tanf(steering));
 
         // New heading
+        // Note: uses agent->length directly, must match wheelbase used in infer_human_actions()
+        // Currently wheelbase == length (set in set_start_position)
         float yaw_rate = (signed_speed * cosf(beta) * tanf(steering)) / agent->length;
 
         // New velocity
@@ -1617,6 +1618,48 @@ void move_dynamics(Drive *env, int action_idx, int agent_idx) {
         agent->heading_y = sinf(heading);
         agent->vx = new_vx;
         agent->vy = new_vy;
+
+    } else if (env->dynamics_model == DELTA_LOCAL) {
+        float action_dx, action_dy, action_dyaw;
+
+        if (env->action_type == 1) { // continuous
+            float *action_base = (float *)env->actions;
+            action_dx = action_base[action_idx * 3 + 0] * DELTA_MAX_DX;
+            action_dy = action_base[action_idx * 3 + 1] * DELTA_MAX_DY;
+            action_dyaw = action_base[action_idx * 3 + 2] * DELTA_MAX_DYAW;
+        } else { // discrete — independent MultiDiscrete: 3 ints per agent
+            int *action_array = (int *)env->actions;
+            int dx_idx = action_array[action_idx * 3 + 0];
+            int dy_idx = action_array[action_idx * 3 + 1];
+            int yaw_idx = action_array[action_idx * 3 + 2];
+            if (dx_idx < 0 || dx_idx >= NUM_DX_BINS)
+                dx_idx = NUM_DX_BINS / 2;
+            if (dy_idx < 0 || dy_idx >= NUM_DY_BINS)
+                dy_idx = NUM_DY_BINS / 2;
+            if (yaw_idx < 0 || yaw_idx >= NUM_YAW_BINS)
+                yaw_idx = NUM_YAW_BINS / 2;
+
+            action_dx = DELTA_DX_VALUES[dx_idx];
+            action_dy = DELTA_DY_VALUES[dy_idx];
+            action_dyaw = DELTA_YAW_VALUES[yaw_idx];
+        }
+
+        action_dx = clip(action_dx, -DELTA_MAX_DX, DELTA_MAX_DX);
+        action_dy = clip(action_dy, -DELTA_MAX_DY, DELTA_MAX_DY);
+        action_dyaw = normalize_heading(action_dyaw);
+
+        float cos_h = agent->heading_x;
+        float sin_h = agent->heading_y;
+        float global_dx = cos_h * action_dx - sin_h * action_dy;
+        float global_dy = sin_h * action_dx + cos_h * action_dy;
+
+        agent->x += global_dx;
+        agent->y += global_dy;
+        agent->heading = normalize_heading(agent->heading + action_dyaw);
+        agent->heading_x = cosf(agent->heading);
+        agent->heading_y = sinf(agent->heading);
+        agent->vx = global_dx / env->dt;
+        agent->vy = global_dy / env->dt;
     } else {
         // JERK dynamics model
         // Extract action components
@@ -1720,6 +1763,23 @@ void move_dynamics(Drive *env, int action_idx, int agent_idx) {
     return;
 }
 
+void apply_dynamics_noise(Drive *env, int agent_idx) {
+    Entity *agent = &env->entities[agent_idx];
+    if (agent->removed || agent->stopped)
+        return;
+
+    if (env->dynamics_noise_pos > 0.0f) {
+        agent->x += gaussian_noise(env->dynamics_noise_pos);
+        agent->y += gaussian_noise(env->dynamics_noise_pos);
+    }
+    if (env->dynamics_noise_heading > 0.0f) {
+        agent->heading += gaussian_noise(env->dynamics_noise_heading);
+        agent->heading = normalize_heading(agent->heading);
+        agent->heading_x = cosf(agent->heading);
+        agent->heading_y = sinf(agent->heading);
+    }
+}
+
 static inline int is_in_track_to_predicts(Drive *env, int agent_idx) {
     if (env->tracks_to_predict_indices == NULL || env->num_tracks_to_predict == 0) {
         return 0;
@@ -1807,7 +1867,7 @@ void c_get_road_edge_polylines(Drive *env, float *x_out, float *y_out, int *leng
 }
 
 void compute_observations(Drive *env) {
-    int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC;
+    int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES;
     int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS;
     memset(env->observations, 0, max_obs * env->active_agent_count * sizeof(float));
     float (*observations)[max_obs] = (float (*)[max_obs])env->observations;
@@ -1831,27 +1891,33 @@ void compute_observations(Drive *env) {
         float rel_goal_x = goal_x * cos_heading + goal_y * sin_heading;
         float rel_goal_y = -goal_x * sin_heading + goal_y * cos_heading;
 
-        obs[0] = rel_goal_x * 0.005f;
-        obs[1] = rel_goal_y * 0.005f;
-        obs[2] = signed_speed / MAX_SPEED;
-        obs[3] = ego_entity->width / MAX_VEH_WIDTH;
-        obs[4] = ego_entity->length / MAX_VEH_LEN;
-        obs[5] = (ego_entity->collision_state > 0) ? 1.0f : 0.0f;
+        // Conditioning observations (idx 0-3)
+        obs[LAMBDA_CONDITIONING_IDX] = ego_entity->lambda;
+        obs[REWARD_COLLISION_IDX] = ego_entity->reward_collision_cond;
+        obs[REWARD_OFFROAD_COLLISION_IDX] = ego_entity->reward_offroad_cond;
+        obs[REWARD_GOAL_IDX] = ego_entity->reward_goal_cond;
+
+        // Other ego features
+        obs[4] = rel_goal_x * 0.005f;
+        obs[5] = rel_goal_y * 0.005f;
+        obs[6] = signed_speed / MAX_SPEED;
+        obs[7] = ego_entity->width / MAX_VEH_WIDTH;
+        obs[8] = ego_entity->length / MAX_VEH_LEN;
+        obs[9] = (ego_entity->collision_state > 0) ? 1.0f : 0.0f;
 
         if (env->dynamics_model == JERK) {
-            obs[6] = ego_entity->steering_angle / M_PI;
+            obs[10] = ego_entity->steering_angle / M_PI;
             // Asymmetric normalization for a_long to match action space
-            obs[7] =
+            obs[11] =
                 (ego_entity->a_long < 0) ? ego_entity->a_long / (-JERK_LONG[0]) : ego_entity->a_long / JERK_LONG[3];
-            obs[8] = ego_entity->a_lat / JERK_LAT[2];
-            obs[9] = (ego_entity->respawn_timestep != -1) ? 1 : 0;
+            obs[12] = ego_entity->a_lat / JERK_LAT[2];
+            obs[13] = 0.0f;
             // Add normalized entity type (VEHICLE=1, PEDESTRIAN=2, CYCLIST=3)
-            obs[10] = ego_entity->type / 3.0f;
+            obs[14] = ego_entity->type / 3.0f;
         } else {
-            obs[6] = (ego_entity->respawn_timestep != -1) ? 1 : 0;
-            obs[7] = ego_entity->type / 3.0f;
+            obs[10] = 0.0f; // Can be deleted later
+            obs[11] = ego_entity->type / 3.0f;
         }
-
         // Relative Pos of other cars
         int obs_idx = ego_dim;
         int cars_seen = 0;
@@ -1869,9 +1935,9 @@ void compute_observations(Drive *env) {
             if (index == env->active_agent_indices[i])
                 continue; // Skip self, but don't increment obs_idx
             Entity *other_entity = &env->entities[index];
-            if (ego_entity->respawn_timestep != -1)
+            if (ego_entity->removed)
                 continue;
-            if (other_entity->respawn_timestep != -1)
+            if (other_entity->removed)
                 continue;
             // Store original relative positions
             float dx = other_entity->x - ego_entity->x;
@@ -1883,10 +1949,10 @@ void compute_observations(Drive *env) {
             float rel_x = dx * cos_heading + dy * sin_heading;
             float rel_y = -dx * sin_heading + dy * cos_heading;
             // Store observations with correct indexing
-            obs[obs_idx] = rel_x * 0.02f;
-            obs[obs_idx + 1] = rel_y * 0.02f;
-            obs[obs_idx + 2] = other_entity->width / MAX_VEH_WIDTH;
-            obs[obs_idx + 3] = other_entity->length / MAX_VEH_LEN;
+            obs[obs_idx] = rel_x * 0.02f + gaussian_noise(env->obs_partner_noise_pos);
+            obs[obs_idx + 1] = rel_y * 0.02f + gaussian_noise(env->obs_partner_noise_pos);
+            obs[obs_idx + 2] = other_entity->width / MAX_VEH_WIDTH + gaussian_noise(env->obs_partner_noise_pos);
+            obs[obs_idx + 3] = other_entity->length / MAX_VEH_LEN + gaussian_noise(env->obs_partner_noise_pos);
             // relative heading
             float rel_heading_x =
                 other_entity->heading_x * ego_entity->heading_x +
@@ -1904,7 +1970,7 @@ void compute_observations(Drive *env) {
             float other_v_dot_heading =
                 other_entity->vx * other_entity->heading_x + other_entity->vy * other_entity->heading_y;
             float other_signed_speed = copysignf(other_speed_magnitude, other_v_dot_heading);
-            obs[obs_idx + 6] = other_signed_speed / MAX_SPEED;
+            obs[obs_idx + 6] = other_signed_speed / MAX_SPEED + gaussian_noise(env->obs_partner_noise_speed);
             cars_seen++;
             obs_idx += 7; // Move to next observation slot
         }
@@ -2034,17 +2100,10 @@ void c_reset(Drive *env) {
     for (int x = 0; x < env->active_agent_count; x++) {
         env->logs[x] = (Log){0};
         int agent_idx = env->active_agent_indices[x];
-        env->entities[agent_idx].respawn_timestep = -1;
-        env->entities[agent_idx].respawn_count = 0;
-        env->entities[agent_idx].collided_before_goal = 0;
+        env->entities[agent_idx].failure_before_goal = 0;
         env->entities[agent_idx].goals_reached_this_episode = 0.0f;
-        // Initialize to 1 because there is one goal in the data file
         env->entities[agent_idx].goals_sampled_this_episode = 1.0f;
         env->entities[agent_idx].current_goal_reached = 0;
-        env->entities[agent_idx].metrics_array[COLLISION_IDX] = 0.0f;
-        env->entities[agent_idx].metrics_array[OFFROAD_IDX] = 0.0f;
-        env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX] = 0.0f;
-        env->entities[agent_idx].metrics_array[LANE_ALIGNED_IDX] = 0.0f;
         env->entities[agent_idx].stopped = 0;
         env->entities[agent_idx].removed = 0;
 
@@ -2053,39 +2112,129 @@ void c_reset(Drive *env) {
             env->entities[agent_idx].goal_position_y = env->entities[agent_idx].init_goal_y;
         }
 
+        // Conditioning
+        // printf("lam %f \n", env->lambda_value);
+
+        if (env->fix_lambdas) {
+            env->entities[agent_idx].lambda = env->lambda_value;
+        } else {
+            env->entities[agent_idx].lambda = (float)rand() / (float)RAND_MAX * 0.5;
+        }
+
+        if (env->fix_rewards) {
+            env->entities[agent_idx].reward_collision_cond = env->reward_vehicle_collision;
+            env->entities[agent_idx].reward_offroad_cond = env->reward_offroad_collision;
+            env->entities[agent_idx].reward_goal_cond = env->reward_goal;
+        } else {
+            float u = (float)rand() / (float)RAND_MAX;
+            float range = 0.1f - env->reward_vehicle_collision;
+            env->entities[agent_idx].reward_collision_cond = env->reward_vehicle_collision + u * range;
+
+            u = (float)rand() / (float)RAND_MAX;
+            range = 0.001f - env->reward_offroad_collision;
+            env->entities[agent_idx].reward_offroad_cond = env->reward_offroad_collision + u * range;
+            env->entities[agent_idx].reward_goal_cond = env->reward_goal;
+        }
         compute_agent_metrics(env, agent_idx);
     }
     compute_observations(env);
 }
 
-void respawn_agent(Drive *env, int agent_idx) {
-    env->entities[agent_idx].x = env->entities[agent_idx].traj_x[0];
-    env->entities[agent_idx].y = env->entities[agent_idx].traj_y[0];
-    env->entities[agent_idx].heading = env->entities[agent_idx].traj_heading[0];
-    env->entities[agent_idx].heading_x = cosf(env->entities[agent_idx].heading);
-    env->entities[agent_idx].heading_y = sinf(env->entities[agent_idx].heading);
-    env->entities[agent_idx].vx = env->entities[agent_idx].traj_vx[0];
-    env->entities[agent_idx].vy = env->entities[agent_idx].traj_vy[0];
-    env->entities[agent_idx].metrics_array[COLLISION_IDX] = 0.0f;
-    env->entities[agent_idx].metrics_array[OFFROAD_IDX] = 0.0f;
-    env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX] = 0.0f;
-    env->entities[agent_idx].metrics_array[LANE_ALIGNED_IDX] = 0.0f;
-
-    env->entities[agent_idx].respawn_timestep = env->timestep;
-    env->entities[agent_idx].collided_before_goal = 0;
-    env->entities[agent_idx].stopped = 0;
-    env->entities[agent_idx].removed = 0;
-    env->entities[agent_idx].a_long = 0.0f;
-    env->entities[agent_idx].a_lat = 0.0f;
-    env->entities[agent_idx].jerk_long = 0.0f;
-    env->entities[agent_idx].jerk_lat = 0.0f;
-    env->entities[agent_idx].steering_angle = 0.0f;
+static void override_action_with_expert(Drive *env, int action_idx, int agent_idx, int t) {
+    Entity *agent = &env->entities[agent_idx];
+
+    if (t < 0 || t >= agent->array_size)
+        return;
+
+    if (env->dynamics_model == DELTA_LOCAL) {
+        if (agent->expert_delta_x == NULL)
+            return;
+        if (agent->expert_delta_x[t] == -1.0f && agent->expert_delta_y[t] == -1.0f)
+            return;
+
+        if (env->action_type == 1) {
+            float *action_base = (float *)env->actions;
+            action_base[action_idx * 3 + 0] = agent->expert_delta_x[t] / DELTA_MAX_DX;
+            action_base[action_idx * 3 + 1] = agent->expert_delta_y[t] / DELTA_MAX_DY;
+            action_base[action_idx * 3 + 2] = agent->expert_delta_yaw[t] / DELTA_MAX_DYAW;
+        } else {
+            int best_dx = 0, best_dy = 0, best_yaw = 0;
+            for (int j = 1; j < NUM_DX_BINS; j++)
+                if (fabsf(agent->expert_delta_x[t] - DELTA_DX_VALUES[j]) <
+                    fabsf(agent->expert_delta_x[t] - DELTA_DX_VALUES[best_dx]))
+                    best_dx = j;
+            for (int j = 1; j < NUM_DY_BINS; j++)
+                if (fabsf(agent->expert_delta_y[t] - DELTA_DY_VALUES[j]) <
+                    fabsf(agent->expert_delta_y[t] - DELTA_DY_VALUES[best_dy]))
+                    best_dy = j;
+            for (int j = 1; j < NUM_YAW_BINS; j++)
+                if (fabsf(agent->expert_delta_yaw[t] - DELTA_YAW_VALUES[j]) <
+                    fabsf(agent->expert_delta_yaw[t] - DELTA_YAW_VALUES[best_yaw]))
+                    best_yaw = j;
+            int *action_array = (int *)env->actions;
+            action_array[action_idx * 3 + 0] = best_dx;
+            action_array[action_idx * 3 + 1] = best_dy;
+            action_array[action_idx * 3 + 2] = best_yaw;
+        }
+    } else {
+        if (agent->expert_accel == NULL || agent->expert_steering == NULL)
+            return;
+        if (agent->expert_accel[t] == -1.0f || agent->expert_steering[t] == -1.0f)
+            return;
+
+        if (env->action_type == 1) {
+            float (*action_array_f)[2] = (float (*)[2])env->actions;
+            action_array_f[action_idx][0] = agent->expert_accel[t] / ACCEL_MAX;
+            action_array_f[action_idx][1] = agent->expert_steering[t] / STEER_MAX;
+        } else {
+            int best_accel_idx = 0;
+            float min_accel_diff = fabsf(agent->expert_accel[t] - ACCELERATION_VALUES[0]);
+            for (int j = 1; j < NUM_ACCEL_BINS; j++) {
+                float diff = fabsf(agent->expert_accel[t] - ACCELERATION_VALUES[j]);
+                if (diff < min_accel_diff) {
+                    min_accel_diff = diff;
+                    best_accel_idx = j;
+                }
+            }
+            int best_steer_idx = 0;
+            float min_steer_diff = fabsf(agent->expert_steering[t] - STEERING_VALUES[0]);
+            for (int j = 1; j < NUM_STEER_BINS; j++) {
+                float diff = fabsf(agent->expert_steering[t] - STEERING_VALUES[j]);
+                if (diff < min_steer_diff) {
+                    min_steer_diff = diff;
+                    best_steer_idx = j;
+                }
+            }
+            int *action_array = (int *)env->actions;
+            action_array[action_idx] = best_accel_idx * NUM_STEER_BINS + best_steer_idx;
+        }
+    }
 }
 
 void c_step(Drive *env) {
     memset(env->rewards, 0, env->active_agent_count * sizeof(float));
     memset(env->terminals, 0, env->active_agent_count * sizeof(unsigned char));
     memset(env->truncations, 0, env->active_agent_count * sizeof(unsigned char));
+
+    // If async_resets is off and we already finished, just re-assert done signals
+    if (!env->async_resets && (env->timestep + 1) >= env->episode_length) {
+        for (int i = 0; i < env->active_agent_count; i++) {
+            env->truncations[i] = 1;
+            env->terminals[i] = 1;
+        }
+        return;
+    }
+
+    // printf("t = %d \n", env->timestep);
+
+    // Re-assert terminal for agents already terminated in a prior step
+    for (int i = 0; i < env->active_agent_count; i++) {
+        int agent_idx = env->active_agent_indices[i];
+        if (env->entities[agent_idx].removed) {
+            env->terminals[i] = 1;
+        }
+    }
+
     env->timestep++;
 
     // Move static experts
@@ -2095,51 +2244,73 @@ void c_step(Drive *env) {
             continue;
         move_expert(env, env->actions, expert_idx);
     }
+
     // Process actions for all active agents
     for (int i = 0; i < env->active_agent_count; i++) {
         env->logs[i].score = 0.0f;
         env->logs[i].episode_length += 1;
         int agent_idx = env->active_agent_indices[i];
+
+        if (env->entities[agent_idx].removed)
+            continue;
+
         env->entities[agent_idx].collision_state = 0;
-        float prev_vx = env->entities[agent_idx].vx;
-        float prev_vy = env->entities[agent_idx].vy;
 
-        move_dynamics(env, i, agent_idx);
+        if (env->control_mode == CONTROL_REPLAY_LOGS) {
+            // Teleport agents along their logged trajectories; ignore policy actions
+            move_expert(env, env->actions, agent_idx);
+        } else {
+            if (env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) {
+                override_action_with_expert(env, i, agent_idx, env->timestep - 1);
+            }
+            // Apply actions
+            move_dynamics(env, i, agent_idx);
 
-        // Tiny jerk penalty for smoothness
-        if (env->dynamics_model == CLASSIC) {
-            float delta_vx = env->entities[agent_idx].vx - prev_vx;
-            float delta_vy = env->entities[agent_idx].vy - prev_vy;
-            float jerk_penalty = -0.0002f * sqrtf(delta_vx * delta_vx + delta_vy * delta_vy) / env->dt;
-            env->rewards[i] += jerk_penalty;
-            env->logs[i].episode_return += jerk_penalty;
+            // Apply sensor noise
+            apply_dynamics_noise(env, agent_idx);
         }
     }
 
     // Compute rewards
     for (int i = 0; i < env->active_agent_count; i++) {
         int agent_idx = env->active_agent_indices[i];
+        int agent_is_done = env->terminals[i];
+
         env->entities[agent_idx].collision_state = 0;
+        env->entities[agent_idx].offroad_state = 0;
+
+        if (agent_is_done)
+            continue;
 
         compute_agent_metrics(env, agent_idx);
-        int collision_state = env->entities[agent_idx].collision_state;
 
-        if (collision_state > 0) {
-            if (collision_state == VEHICLE_COLLISION) {
-                env->rewards[i] += env->reward_vehicle_collision;
-                env->logs[i].episode_return += env->reward_vehicle_collision;
-                env->logs[i].collision_rate = 1.0f;
-                env->logs[i].collisions_per_agent += 1.0f;
-            } else if (collision_state == OFFROAD) {
-                env->rewards[i] += env->reward_offroad_collision;
-                env->logs[i].episode_return += env->reward_offroad_collision;
-                env->logs[i].offroad_rate = 1.0f;
-                env->logs[i].offroad_per_agent += 1.0f;
+        int collision_state = env->entities[agent_idx].collision_state;
+        int offroad_state = env->entities[agent_idx].offroad_state;
+
+        if (collision_state == 1 && !agent_is_done) {
+            float r_collision = env->entities[agent_idx].reward_collision_cond;
+            env->rewards[i] += r_collision;
+            env->logs[i].episode_return += r_collision;
+            env->logs[i].collision_rate = 1.0f;
+            env->logs[i].collisions_per_agent += 1.0f;
+            if (env->entities[agent_idx].at_fault_collision_state) {
+                env->logs[i].at_fault_collision_rate = 1.0f;
             }
+        }
+
+        if (offroad_state == 1 && !agent_is_done) {
+            float r_off = env->entities[agent_idx].reward_offroad_cond;
+            env->rewards[i] += r_off;
+            env->logs[i].episode_return += r_off;
+            env->logs[i].offroad_rate = 1.0f;
+            env->logs[i].offroad_per_agent += 1.0f;
+        }
 
-            env->entities[agent_idx].collided_before_goal = 1;
+        if ((collision_state || offroad_state) && !agent_is_done) {
+            env->entities[agent_idx].failure_before_goal = 1;
         }
 
+        // Check if agent reached goal
         float distance_to_goal =
             relative_distance_2d(env->entities[agent_idx].x, env->entities[agent_idx].y,
                                  env->entities[agent_idx].goal_position_x, env->entities[agent_idx].goal_position_y);
@@ -2152,76 +2323,263 @@ void c_step(Drive *env) {
         bool within_speed = current_speed <= env->goal_speed;
 
         if (within_distance && within_speed && !env->entities[agent_idx].current_goal_reached) {
-            if (env->goal_behavior == GOAL_RESPAWN && env->entities[agent_idx].respawn_timestep != -1) {
-                env->rewards[i] += env->reward_goal_post_respawn;
-                env->logs[i].episode_return += env->reward_goal_post_respawn;
-                env->entities[agent_idx].current_goal_reached = 1;
-            } else if (env->goal_behavior == GOAL_GENERATE_NEW && (!env->entities[agent_idx].current_goal_reached)) {
-                env->rewards[i] += env->reward_goal;
-                env->logs[i].episode_return += env->reward_goal;
+            float r_goal = env->entities[agent_idx].reward_goal_cond;
+            env->rewards[i] += r_goal;
+            env->logs[i].episode_return += r_goal;
+            env->entities[agent_idx].goals_reached_this_episode += 1.0f;
+            env->entities[agent_idx].current_goal_reached = 1;
+            env->logs[i].speed_at_goal = current_speed;
+
+            if (env->goal_behavior == GOAL_GENERATE_NEW) {
                 sample_new_goal(env, agent_idx);
                 env->entities[agent_idx].current_goal_reached = 0;
-                env->entities[agent_idx].goals_reached_this_episode += 1.0f;
-            } else { // Zero out the velocity so that the agent stops at the goal
-                env->rewards[i] = env->reward_goal;
-                env->logs[i].episode_return = env->reward_goal;
-                env->entities[agent_idx].stopped = 1;
-                env->entities[agent_idx].vx = env->entities[agent_idx].vy = 0.0f;
-                env->entities[agent_idx].goals_reached_this_episode += 1.0f;
             }
-            env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX] = 1.0f;
-            env->logs[i].speed_at_goal = current_speed;
         }
-
-        int lane_aligned = env->entities[agent_idx].metrics_array[LANE_ALIGNED_IDX];
-        env->logs[i].lane_alignment_rate = lane_aligned;
     }
-
-    if (env->goal_behavior == GOAL_RESPAWN) {
+    // Handle agents
+    if (env->goal_behavior == GOAL_REMOVE) {
         for (int i = 0; i < env->active_agent_count; i++) {
             int agent_idx = env->active_agent_indices[i];
-            int reached_goal = env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX];
-            if (reached_goal) {
+            if (env->entities[agent_idx].current_goal_reached) {
                 env->terminals[i] = 1;
-                respawn_agent(env, agent_idx);
-                env->entities[agent_idx].respawn_count++;
+                env->entities[agent_idx].removed = 1;
+                env->entities[agent_idx].stopped = 1;
+                env->entities[agent_idx].vx = env->entities[agent_idx].vy = 0.0f;
+                env->entities[agent_idx].x = INVALID_POSITION;
+                env->entities[agent_idx].y = INVALID_POSITION;
             }
         }
+
     } else if (env->goal_behavior == GOAL_STOP) {
         for (int i = 0; i < env->active_agent_count; i++) {
             int agent_idx = env->active_agent_indices[i];
-            int reached_goal = env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX];
+            int reached_goal = env->entities[agent_idx].current_goal_reached;
             if (reached_goal) {
+                env->terminals[i] = 1; // Mark agent as done
                 env->entities[agent_idx].stopped = 1;
                 env->entities[agent_idx].vx = env->entities[agent_idx].vy = 0.0f;
             }
         }
     }
 
-    // Episode boundary after this step: treat time-limit and early-termination as truncation.
-    // `timestep` is incremented at step start, so truncate when `(timestep + 1) >= episode_length`.
-    int originals_remaining = 0;
-    for (int i = 0; i < env->active_agent_count; i++) {
-        int agent_idx = env->active_agent_indices[i];
-        if (env->entities[agent_idx].respawn_count == 0) {
-            originals_remaining = 1;
-            break;
+    // Truncate episode when all agents are terminated
+    int all_terminated = 1;
+    if (env->termination_mode == 1) {
+        for (int i = 0; i < env->active_agent_count; i++) {
+            if (!env->terminals[i]) {
+                all_terminated = 0;
+                break;
+            }
         }
     }
-    int reached_time_limit = (env->timestep + 1) >= env->episode_length;
-    int reached_early_termination = (!originals_remaining && env->termination_mode == 1);
-    if (reached_time_limit || reached_early_termination) {
+    int reached_max_episode_len = (env->timestep + 1) >= env->episode_length;
+    if (reached_max_episode_len || (all_terminated && env->termination_mode == 1)) {
         for (int i = 0; i < env->active_agent_count; i++) {
             env->truncations[i] = 1;
         }
         add_log(env);
-        c_reset(env);
+        if (env->async_resets) {
+            // printf("[async_resets=1] Episode done at t=%d, resetting\n", env->timestep);
+            c_reset(env);
+        }
         return;
     }
 
     compute_observations(env);
 }
 
+void c_collect_expert_data(Drive *env, float *expert_actions_discrete_out, float *expert_obs_out) {
+    int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES;
+    int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS;
+
+    int saved_control_mode = env->control_mode;
+    int saved_goal_behavior = env->goal_behavior;
+    int saved_episode_length = env->episode_length;
+    int saved_termination_mode = env->termination_mode;
+
+    env->control_mode = CONTROL_INFERRED_EXPERT_ACTIONS;
+    env->episode_length = TRAJECTORY_LENGTH + 1;
+    env->goal_behavior = GOAL_STOP;
+    env->termination_mode = 0;
+    env->init_steps = 0;
+
+    c_reset(env); // Reset all agents to their initial position
+
+    for (int t = 0; t < TRAJECTORY_LENGTH; t++) {
+        int obs_offset = t * env->active_agent_count * max_obs;
+        memcpy(&expert_obs_out[obs_offset], env->observations, env->active_agent_count * max_obs * sizeof(float));
+
+        // Write expert actions into env->actions for this timestep
+        for (int i = 0; i < env->active_agent_count; i++) {
+            int agent_idx = env->active_agent_indices[i];
+            override_action_with_expert(env, i, agent_idx, t);
+        }
+
+        for (int i = 0; i < env->active_agent_count; i++) {
+            int agent_idx = env->active_agent_indices[i];
+            Entity *agent = &env->entities[agent_idx];
+
+            if (agent->inferred_traj_x != NULL) {
+                agent->inferred_traj_x[t] = agent->x;
+                agent->inferred_traj_y[t] = agent->y;
+            }
+        }
+        if (t < TRAJECTORY_LENGTH - 1) {
+            c_step(env);
+
+            if (COLLECT_EXPERT_TELEPORT) {
+                // Overwrite active agent positions with ground truth after c_step
+                // so that obs are computed from true positions, while static agents
+                // were advanced by c_step as a side effect.
+                for (int i = 0; i < env->active_agent_count; i++) {
+                    int agent_idx = env->active_agent_indices[i];
+                    Entity *agent = &env->entities[agent_idx];
+                    int next = t + 1;
+                    if (next < agent->array_size && agent->traj_valid && agent->traj_valid[next]) {
+                        agent->x = agent->traj_x[next];
+                        agent->y = agent->traj_y[next];
+                        agent->z = agent->traj_z[next];
+                        agent->heading = agent->traj_heading[next];
+                        agent->heading_x = cosf(agent->heading);
+                        agent->heading_y = sinf(agent->heading);
+                        agent->vx = agent->traj_vx[next];
+                        agent->vy = agent->traj_vy[next];
+                        agent->vz = agent->traj_vz[next];
+                    }
+                }
+                compute_observations(env);
+            }
+        }
+
+        // Record actions
+        for (int i = 0; i < env->active_agent_count; i++) {
+            int agent_idx = env->active_agent_indices[i];
+            Entity *agent = &env->entities[agent_idx];
+
+            int disc_off = t * env->active_agent_count + i;
+
+            bool valid;
+            if (env->dynamics_model == DELTA_LOCAL) {
+                valid = (t < agent->array_size && agent->expert_delta_x != NULL && agent->expert_delta_x[t] != -1.0f);
+            } else {
+                valid = (t < agent->array_size && agent->expert_accel != NULL && agent->expert_accel[t] != -1.0f);
+            }
+
+            if (valid) {
+                if (env->dynamics_model == DELTA_LOCAL) {
+                    int *ai = (int *)env->actions;
+                    int dx_idx = ai[i * 3 + 0];
+                    int dy_idx = ai[i * 3 + 1];
+                    int yaw_idx = ai[i * 3 + 2];
+                    if (env->action_type == 1) {
+                        float *ab = (float *)env->actions;
+                        float dx = ab[i * 3 + 0] * DELTA_MAX_DX;
+                        float dy = ab[i * 3 + 1] * DELTA_MAX_DY;
+                        float dyaw = ab[i * 3 + 2] * DELTA_MAX_DYAW;
+                        dx_idx = 0;
+                        dy_idx = 0;
+                        yaw_idx = 0;
+                        for (int j = 1; j < NUM_DX_BINS; j++)
+                            if (fabsf(dx - DELTA_DX_VALUES[j]) < fabsf(dx - DELTA_DX_VALUES[dx_idx]))
+                                dx_idx = j;
+                        for (int j = 1; j < NUM_DY_BINS; j++)
+                            if (fabsf(dy - DELTA_DY_VALUES[j]) < fabsf(dy - DELTA_DY_VALUES[dy_idx]))
+                                dy_idx = j;
+                        for (int j = 1; j < NUM_YAW_BINS; j++)
+                            if (fabsf(dyaw - DELTA_YAW_VALUES[j]) < fabsf(dyaw - DELTA_YAW_VALUES[yaw_idx]))
+                                yaw_idx = j;
+                    }
+                    int base = (t * env->active_agent_count + i) * 3;
+                    expert_actions_discrete_out[base + 0] = (float)dx_idx;
+                    expert_actions_discrete_out[base + 1] = (float)dy_idx;
+                    expert_actions_discrete_out[base + 2] = (float)yaw_idx;
+                } else {
+                    if (env->action_type == 1) {
+                        float (*af)[2] = (float (*)[2])env->actions;
+                        float accel = af[i][0] * ACCEL_MAX;
+                        float steer = af[i][1] * STEER_MAX;
+                        int best_a = 0, best_s = 0;
+                        for (int j = 1; j < NUM_ACCEL_BINS; j++) {
+                            if (fabsf(accel - ACCELERATION_VALUES[j]) < fabsf(accel - ACCELERATION_VALUES[best_a]))
+                                best_a = j;
+                        }
+                        for (int j = 1; j < NUM_STEER_BINS; j++) {
+                            if (fabsf(steer - STEERING_VALUES[j]) < fabsf(steer - STEERING_VALUES[best_s]))
+                                best_s = j;
+                        }
+                        expert_actions_discrete_out[disc_off] = (float)(best_a * NUM_STEER_BINS + best_s);
+                    } else {
+                        int *ai = (int *)env->actions;
+                        expert_actions_discrete_out[disc_off] = (float)ai[i];
+                    }
+                }
+            } else {
+                if (env->dynamics_model == DELTA_LOCAL) {
+                    int base = (t * env->active_agent_count + i) * 3;
+                    expert_actions_discrete_out[base + 0] = -1.0f;
+                    expert_actions_discrete_out[base + 1] = -1.0f;
+                    expert_actions_discrete_out[base + 2] = -1.0f;
+                } else {
+                    int disc_off = t * env->active_agent_count + i;
+                    expert_actions_discrete_out[disc_off] = -1.0f;
+                }
+            }
+        }
+    }
+
+    // Filter data: Compute per-agent ADE and mark unfit trajectories
+    for (int i = 0; i < env->active_agent_count; i++) {
+        int idx = env->active_agent_indices[i];
+        Entity *e = &env->entities[idx];
+        float total_error = 0.0f;
+        int count = 0;
+        int is_static = 1;
+
+        if (e->inferred_traj_x && e->traj_valid) {
+            for (int t = 0; t < TRAJECTORY_LENGTH; t++) {
+                if (!e->traj_valid[t])
+                    continue;
+                float dx = e->inferred_traj_x[t] - e->traj_x[t];
+                float dy = e->inferred_traj_y[t] - e->traj_y[t];
+                // printf("t %d error %.2f\n", t, sqrtf(dx * dx + dy * dy));
+                total_error += sqrtf(dx * dx + dy * dy);
+                count++;
+                if (count > 1 && (e->traj_x[t] != e->traj_x[0] || e->traj_y[t] != e->traj_y[0]))
+                    is_static = 0;
+            }
+        }
+
+        e->inferred_ade = (count > 0) ? total_error / count : -1.0f;
+
+        int min_valid = 10;
+        int unfit;
+        if (COLLECT_EXPERT_TELEPORT) {
+            unfit = (count < min_valid || is_static);
+        } else {
+            unfit = (e->inferred_ade < 0.0f || e->inferred_ade > ADE_THRESHOLD || count < min_valid || is_static);
+        }
+
+        if (unfit) {
+            for (int t = 0; t < TRAJECTORY_LENGTH; t++) {
+                if (env->dynamics_model == DELTA_LOCAL) {
+                    int base = (t * env->active_agent_count + i) * 3;
+                    expert_actions_discrete_out[base + 0] = -1.0f;
+                    expert_actions_discrete_out[base + 1] = -1.0f;
+                    expert_actions_discrete_out[base + 2] = -1.0f;
+                } else {
+                    int disc_off = t * env->active_agent_count + i;
+                    expert_actions_discrete_out[disc_off] = -1.0f;
+                }
+            }
+        }
+    }
+
+    env->control_mode = saved_control_mode;
+    env->goal_behavior = saved_goal_behavior;
+    env->episode_length = saved_episode_length;
+    env->termination_mode = saved_termination_mode;
+}
+
 typedef struct Client Client;
 
 struct Client {
@@ -2244,6 +2602,51 @@ struct Client {
     int xvfb_display_num;
 };
 
+void stop_recorder(Client *client) {
+    if (client->recorder_pid > 0) {
+        close(client->recorder_pipefd[1]);
+        waitpid(client->recorder_pid, NULL, 0);
+        client->recorder_pid = 0;
+    }
+}
+
+void start_recorder(Client *client, const char *scenario_id, int width, int height) {
+    stop_recorder(client);
+
+    if (pipe(client->recorder_pipefd) == -1) {
+        fprintf(stderr, "Failed to create pipe for recorder\n");
+        return;
+    }
+
+    char size_str[64];
+    snprintf(size_str, sizeof(size_str), "%dx%d", width, height);
+
+    char filename[256];
+    snprintf(filename, sizeof(filename), "%s.mp4", scenario_id);
+
+    client->recorder_pid = fork();
+    if (client->recorder_pid == -1) {
+        fprintf(stderr, "Failed to fork recorder\n");
+        close(client->recorder_pipefd[0]);
+        close(client->recorder_pipefd[1]);
+        return;
+    }
+
+    if (client->recorder_pid == 0) {
+        close(client->recorder_pipefd[1]);
+        dup2(client->recorder_pipefd[0], STDIN_FILENO);
+        close(client->recorder_pipefd[0]);
+        for (int fd = 3; fd < 256; fd++)
+            close(fd);
+        execlp("ffmpeg", "ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgba", "-s", size_str, "-r", "30", "-i", "-",
+               "-c:v", "libx264", "-pix_fmt", "yuv420p", "-preset", "ultrafast", "-crf", "23", "-loglevel", "error",
+               filename, NULL);
+        fprintf(stderr, "execlp ffmpeg failed\n");
+        _exit(1);
+    }
+    close(client->recorder_pipefd[0]);
+}
+
 Client *make_client(Drive *env) {
 
     Client *client = (Client *)calloc(1, sizeof(Client));
@@ -2308,22 +2711,32 @@ Client *make_client(Drive *env) {
         client->camera.projection = CAMERA_PERSPECTIVE;
 
     } else { // Headless rendering
-        SetConfigFlags(FLAG_WINDOW_HIDDEN);
-        SetTargetFPS(6000);
-
-        float map_width = env->grid_map->bottom_right_x - env->grid_map->top_left_x;
-        float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y;
-        float scale = 6.0f; // Controls the resolution of the output video
-        int img_width = (int)roundf(map_width * scale / 2.0f) * 2;
-        int img_height = (int)roundf(map_height * scale / 2.0f) * 2;
+        if (env->sdc_track_index >= 0 && env->control_mode == CONTROL_SDC_ONLY) {
+            // Fix to square around target agent
+            client->width = 720;
+            client->height = 720;
+        } else {
+            // Full map size
+            float map_width = env->grid_map->bottom_right_x - env->grid_map->top_left_x;
+            float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y;
+            float scale = 6.0f; // Controls the resolution of the output video
+            int img_width = (int)roundf(map_width * scale / 2.0f) * 2;
+            int img_height = (int)roundf(map_height * scale / 2.0f) * 2;
 
-        client->width = img_width;
-        client->height = img_height;
+            client->width = img_width;
+            client->height = img_height;
+        }
     }
 
     SetTraceLogLevel(LOG_WARNING); // Only show warnings and errors
     InitWindow(client->width, client->height, "PufferDrive");
 
+    if (!IsWindowReady()) {
+        fprintf(stderr, "WARNING: Failed to initialize render window. Rendering disabled.\n");
+        free(client);
+        return NULL;
+    }
+
     // Load assets
     client->cars[0] = LoadModel("resources/drive/RedCar.glb");
     client->cars[1] = LoadModel("resources/drive/WhiteCar.glb");
@@ -2339,42 +2752,6 @@ Client *make_client(Drive *env) {
         client->car_assignments[i] = (rand() % 4) + 1;
     }
 
-    // Set up ffmpeg process for recording
-    if (env->render_mode == RENDER_HEADLESS) {
-        if (pipe(client->recorder_pipefd) == -1) {
-            fprintf(stderr, "Failed to create pipe\n");
-            free(client);
-            return NULL;
-        }
-
-        char size_str[64];
-        snprintf(size_str, sizeof(size_str), "%dx%d", (int)client->width, (int)client->height);
-
-        char filename[256];
-        snprintf(filename, sizeof(filename), "%s.mp4", env->scenario_id);
-
-        client->recorder_pid = fork();
-        if (client->recorder_pid == -1) {
-            fprintf(stderr, "Failed to fork\n");
-            free(client);
-            return NULL;
-        }
-
-        if (client->recorder_pid == 0) { // Child process
-            close(client->recorder_pipefd[1]);
-            dup2(client->recorder_pipefd[0], STDIN_FILENO);
-            close(client->recorder_pipefd[0]);
-            for (int fd = 3; fd < 256; fd++)
-                close(fd);
-            execlp("ffmpeg", "ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgba", "-s", size_str, "-r", "30", "-i",
-                   "-", "-c:v", "libx264", "-pix_fmt", "yuv420p", "-preset", "ultrafast", "-crf", "23", "-loglevel",
-                   "error", filename, NULL);
-            fprintf(stderr, "execlp ffmpeg failed\n");
-            _exit(1);
-        }
-        close(client->recorder_pipefd[0]);
-    }
-
     return client;
 }
 
@@ -2463,7 +2840,7 @@ void draw_agent_obs(Drive *env, int agent_index, int mode, int obs_only, int las
         return;
     }
 
-    int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC;
+    int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES;
     int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS;
     float (*observations)[max_obs] = (float (*)[max_obs])env->observations;
     float *agent_obs = &observations[agent_index][0];
@@ -2713,8 +3090,47 @@ void draw_road_edge(Drive *env, float start_x, float start_y, float end_x, float
     DrawTriangle3D(t4, t1, b1, CURB_SIDE);
 }
 
+void draw_inferred_trajectory(Drive *env, int agent_array_idx) {
+    int idx = env->active_agent_indices[agent_array_idx];
+    Entity *e = &env->entities[idx];
+    if (!e->inferred_traj_x || !e->traj_valid)
+        return;
+
+    float zg = Z_ROAD_MARKINGS, zs = Z_ROAD_MARKINGS + 0.01f;
+
+    for (int t = 0; t < e->array_size - 1; t++) {
+        if (!e->traj_valid[t])
+            continue;
+
+        // Ground truth (green)
+        DrawSphere((Vector3){e->traj_x[t], e->traj_y[t], zg}, 0.15f, LIGHTGREEN);
+        if (e->traj_valid[t + 1])
+            DrawLine3D((Vector3){e->traj_x[t], e->traj_y[t], zg}, (Vector3){e->traj_x[t + 1], e->traj_y[t + 1], zg},
+                       Fade(LIGHTGREEN, 0.6f));
+
+        // Inferred (purple)
+        DrawSphere((Vector3){e->inferred_traj_x[t], e->inferred_traj_y[t], zs}, 0.2f, LIGHT_PURPLE);
+        DrawLine3D((Vector3){e->inferred_traj_x[t], e->inferred_traj_y[t], zs},
+                   (Vector3){e->inferred_traj_x[t + 1], e->inferred_traj_y[t + 1], zs}, Fade(LIGHT_PURPLE, 0.6f));
+
+        // Error shading (red)
+        if (e->traj_valid[t + 1]) {
+            Vector3 a = {e->traj_x[t], e->traj_y[t], zg};
+            Vector3 b = {e->inferred_traj_x[t], e->inferred_traj_y[t], zg};
+            Vector3 c = {e->traj_x[t + 1], e->traj_y[t + 1], zg};
+            Vector3 d = {e->inferred_traj_x[t + 1], e->inferred_traj_y[t + 1], zg};
+            Color err = Fade(PUFF_RED, 0.15f);
+            DrawTriangle3D(a, b, c, err);
+            DrawTriangle3D(c, b, d, err);
+            DrawTriangle3D(c, b, a, err);
+            DrawTriangle3D(d, b, c, err);
+        }
+    }
+}
+
 void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, int show_grid) {
 
+    int has_inferred = 0;
     if (show_grid) {
         float grid_start_x = env->grid_map->top_left_x;
         float grid_start_y = env->grid_map->bottom_right_y;
@@ -2728,20 +3144,46 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
         }
     }
 
-    // Draw a grid to help with orientation
+    // Draw expert trajectories for verification modes
+    if (env->control_mode == CONTROL_REPLAY_LOGS || env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) {
+        Color traj_color = (env->control_mode == CONTROL_REPLAY_LOGS) ? EXPERT_REPLAY : LIGHT_PURPLE;
+        for (int i = 0; i < env->active_agent_count; i++) {
+            int idx = env->active_agent_indices[i];
+            Entity *e = &env->entities[idx];
+            for (int t = env->init_steps; t < e->array_size - 1; t++) {
+                if (e->traj_valid[t] && e->traj_valid[t + 1]) {
+                    DrawLine3D((Vector3){e->traj_x[t], e->traj_y[t], Z_ROAD_MARKINGS},
+                               (Vector3){e->traj_x[t + 1], e->traj_y[t + 1], Z_ROAD_MARKINGS}, Fade(traj_color, 0.4f));
+                }
+                if (e->traj_valid[t]) {
+                    DrawSphere((Vector3){e->traj_x[t], e->traj_y[t], Z_ROAD_MARKINGS}, 0.15f, traj_color);
+                }
+            }
+        }
+
+        if (env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) {
+            has_inferred = 1;
+            for (int i = 0; i < env->active_agent_count; i++) {
+                draw_inferred_trajectory(env, i);
+            }
+        }
+    }
+    // Draw entities
     for (int i = 0; i < env->num_entities; i++) {
         // Draw objects
         if (env->entities[i].type == VEHICLE || env->entities[i].type == PEDESTRIAN ||
             env->entities[i].type == CYCLIST) {
-            // Check if this vehicle is an active agent
+
             bool is_active_agent = false;
             bool is_static_agent = false;
             int agent_index = -1;
             for (int j = 0; j < env->active_agent_count; j++) {
                 if (env->active_agent_indices[j] == i) {
-                    is_active_agent = true;
                     agent_index = j;
-                    break;
+                    if (!env->entities[agent_index].removed) {
+                        is_active_agent = true;
+                        break;
+                    }
                 }
             }
 
@@ -2759,9 +3201,14 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
                 }
             }
 
-            if ((!is_active_agent && !is_static_agent) || env->entities[i].respawn_timestep != -1) {
+            if (!is_active_agent && !is_static_agent) {
                 continue;
             }
+
+            if (env->entities[i].removed) {
+                continue;
+            }
+
             Vector3 position;
             float heading;
             position = (Vector3){env->entities[i].x, env->entities[i].y, Z_AGENTS};
@@ -2795,7 +3242,7 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
                 };
 
                 if (agent_index == env->human_agent_idx &&
-                    !env->entities[agent_index].metrics_array[REACHED_GOAL_IDX]) {
+                    !env->entities[env->active_agent_indices[agent_index]].current_goal_reached) {
                     draw_agent_obs(env, agent_index, mode, obs_only, lasers);
                 }
 
@@ -2812,7 +3259,11 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
                         agent_color = EXPERT_REPLAY;
                 }
                 if (is_active_agent) {
-                    if (env->entities[i].type == PEDESTRIAN)
+                    if (env->control_mode == CONTROL_REPLAY_LOGS)
+                        agent_color = EXPERT_REPLAY;
+                    else if (env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS)
+                        agent_color = LIGHT_PURPLE;
+                    else if (env->entities[i].type == PEDESTRIAN)
                         agent_color = LIGHT_ORANGE;
                     else if (env->entities[i].type == CYCLIST)
                         agent_color = LIGHT_PURPLE;
@@ -2822,6 +3273,9 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
                 if (is_active_agent && env->entities[i].collision_state > 0)
                     agent_color = RED;
 
+                if (is_active_agent && env->entities[i].offroad_state > 0)
+                    agent_color = LIGHTYELLOW;
+
                 rlPushMatrix();
                 rlTranslatef(position.x, position.y, position.z);
                 rlRotatef(heading * RAD2DEG, 0.0f, 0.0f, 1.0f);
@@ -2829,13 +3283,30 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
                 DrawCubeWires((Vector3){0.0f, 0.0f, 0.0f}, size.x, size.y, 1.0f, agent_color);
                 rlPopMatrix();
 
-                // Draw a heading arrow pointing forward
+                // Draw a heading arrow pointing forward, scaled by speed so we can see how fast the agent is going
+                float agent_speed =
+                    sqrtf(env->entities[i].vx * env->entities[i].vx + env->entities[i].vy * env->entities[i].vy);
+                float speed_frac = fminf(agent_speed / MAX_SPEED, 1.0f);
+                float arrow_len = half_len * 1.5f + speed_frac * 15.0f;
+                float arrow_head_size = 0.85f;
+
                 Vector3 arrowStart = position;
-                Vector3 arrowEnd = {position.x + cos_heading * half_len * 1.5f, // extend arrow beyond car
-                                    position.y + sin_heading * half_len * 1.5f, position.z};
+                Vector3 arrowEnd = {position.x + cos_heading * arrow_len, position.y + sin_heading * arrow_len,
+                                    position.z};
 
+                rlSetLineWidth(2.5f);
                 DrawLine3D(arrowStart, arrowEnd, agent_color);
-                DrawSphere(arrowEnd, 0.2f, agent_color); // arrow tip
+
+                // Arrowhead wings
+                float perp_x = -sin_heading * arrow_head_size;
+                float perp_y = cos_heading * arrow_head_size;
+                Vector3 wing1 = {arrowEnd.x - cos_heading * arrow_head_size + perp_x,
+                                 arrowEnd.y - sin_heading * arrow_head_size + perp_y, position.z};
+                Vector3 wing2 = {arrowEnd.x - cos_heading * arrow_head_size - perp_x,
+                                 arrowEnd.y - sin_heading * arrow_head_size - perp_y, position.z};
+                DrawLine3D(arrowEnd, wing1, agent_color);
+                DrawLine3D(arrowEnd, wing2, agent_color);
+                rlSetLineWidth(1.0f);
 
             } else { // Agent view
                 rlPushMatrix();
@@ -2846,19 +3317,19 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
                 // Select car model (skip index 0)
                 Model car_model = client->cars[(i % 5) + 1]; // Cycles through indices 1-5
 
-                if (agent_index == env->human_agent_idx) {
-                    car_model = client->cars[0]; // Ego agent always uses red car
+                if (env->control_mode == CONTROL_REPLAY_LOGS || env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) {
+                    car_model = client->cars[1]; // White car for verification modes
+                } else if (agent_index == env->human_agent_idx) {
+                    car_model = client->cars[0];
                 } else if (is_active_agent) {
-
                     car_model = client->cars[(i % 5) + 1];
-
                     if (env->entities[i].collision_state > 0) {
-                        car_model = client->cars[0]; // Collided agents use red
+                        car_model = client->cars[0];
                     }
                 }
                 // Draw obs for selected agent index
                 if (agent_index == env->human_agent_idx &&
-                    (!env->entities[agent_index].metrics_array[REACHED_GOAL_IDX] ||
+                    (!env->entities[env->active_agent_indices[agent_index]].current_goal_reached ||
                      env->goal_behavior == GOAL_GENERATE_NEW || env->goal_behavior == GOAL_STOP)) {
                     draw_agent_obs(env, agent_index, mode, obs_only, lasers);
                 }
@@ -2891,10 +3362,20 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
                     Color wire_color = GRAY;
                     if (!is_active_agent && env->entities[i].mark_as_expert == 1)
                         wire_color = EXPERT_REPLAY;
-                    if (is_active_agent)
-                        wire_color = BLUE; // Policy-controlled
+                    if (is_active_agent) {
+                        if (env->control_mode == CONTROL_REPLAY_LOGS)
+                            wire_color = EXPERT_REPLAY;
+                        else if (env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS)
+                            wire_color = LIGHT_PURPLE;
+                        else
+                            wire_color = BLUE; // Policy-controlled
+                    }
                     if (is_active_agent && env->entities[i].collision_state > 0)
                         wire_color = RED;
+
+                    if (is_active_agent && env->entities[i].collision_state > 0)
+                        wire_color = LIGHTYELLOW;
+
                     rlSetLineWidth(2.0f);
                     for (int j = 0; j < 4; j++) {
                         DrawLine3D(corners[j], corners[(j + 1) % 4], wire_color);
@@ -2923,7 +3404,8 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
             if (!is_active_agent || env->entities[i].valid == 0) {
                 continue;
             }
-            if (!IsKeyDown(KEY_LEFT_CONTROL) && obs_only == 0) {
+            if (!IsKeyDown(KEY_LEFT_CONTROL) && obs_only == 0 && env->control_mode != CONTROL_REPLAY_LOGS &&
+                env->control_mode != CONTROL_INFERRED_EXPERT_ACTIONS) {
                 Color goal_color = DEEPBLUE;
                 if (env->entities[i].type == PEDESTRIAN)
                     goal_color = LIGHT_ORANGE;
@@ -2939,7 +3421,7 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
             }
         }
         // Draw road elements
-        if (env->entities[i].type <= 3 && env->entities[i].type >= 7) {
+        if (env->entities[i].type <= 3 || env->entities[i].type >= 7) {
             continue;
         }
         for (int j = 0; j < env->entities[i].array_size - 1; j++) {
@@ -2969,33 +3451,187 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers,
 
     EndMode3D();
 
-    // Draw track indices for the tracks to predict
-    if (mode == 1 && env->control_mode == CONTROL_WOSAC) {
-        float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y;
-        float pixels_per_world_unit = client->height / map_height;
-
+    // Draw per-agent ADE labels projected to screen for interactive verify mode
+    if (has_inferred) {
         for (int i = 0; i < env->active_agent_count; i++) {
-            // Ignore respawned agents
-            if (env->entities[i].respawn_timestep != -1) {
+            int idx = env->active_agent_indices[i];
+            Entity *e = &env->entities[idx];
+            if (e->inferred_ade < 0)
+                continue;
+            int mid = e->array_size / 2;
+            Vector2 sp = GetWorldToScreen((Vector3){e->traj_x[mid], e->traj_y[mid], Z_AGENT_DETAILS}, client->camera);
+            int sx = (int)sp.x, sy = (int)sp.y;
+            if (sx < 0 || sx > client->width || sy < 0 || sy > client->height)
                 continue;
+            char buf[32];
+            // Check if unfit (same criteria as c_collect_expert_data)
+            int valid_count = 0;
+            int is_static = 1;
+            if (e->traj_valid) {
+                for (int t = 0; t < e->array_size; t++) {
+                    if (!e->traj_valid[t])
+                        continue;
+                    valid_count++;
+                    if (valid_count > 1 && (e->traj_x[t] != e->traj_x[0] || e->traj_y[t] != e->traj_y[0]))
+                        is_static = 0;
+                }
             }
+            int unfit = (e->inferred_ade < 0.0f || e->inferred_ade > ADE_THRESHOLD || valid_count < 3 || is_static);
+            Color label_color = unfit ? PUFF_RED : PUFF_WHITE;
+            snprintf(buf, sizeof(buf), "ADE:%.2fm", e->inferred_ade);
+            DrawText(buf, sx - MeasureText(buf, 16) / 2, sy, 16, label_color);
+        }
+    }
+
+    if (mode == 1) {
+        float cam_x = 0.0f, cam_y = 0.0f;
+        float fovy = env->grid_map->top_left_y - env->grid_map->bottom_right_y;
+
+        if (env->sdc_track_index >= 0 && env->control_mode == CONTROL_SDC_ONLY &&
+            !env->entities[env->sdc_track_index].removed) {
+            cam_x = env->entities[env->sdc_track_index].x;
+            cam_y = env->entities[env->sdc_track_index].y;
+            fovy = 150.0f;
+        }
+        float scale = client->height / fovy;
+
+        for (int i = 0; i < env->active_agent_count; i++) {
             int agent_idx = env->active_agent_indices[i];
-            int womd_track_idx = env->tracks_to_predict_indices[i];
 
-            float raw_x = -env->entities[agent_idx].x * pixels_per_world_unit;
-            float raw_y = env->entities[agent_idx].y * pixels_per_world_unit;
+            if (env->entities[agent_idx].removed || env->entities[agent_idx].x == INVALID_POSITION) {
+                continue;
+            }
 
-            int screen_x = (int)raw_x + client->width / 2 + 20;
-            int screen_y = (int)raw_y + client->height / 2 - 25;
+            int sx = (int)(-(env->entities[agent_idx].x - cam_x) * scale) + client->width / 2 + 20;
+            int sy = (int)((env->entities[agent_idx].y - cam_y) * scale) + client->height / 2 - 25;
+            if (sx < 0 || sx > client->width || sy < 0 || sy > client->height)
+                continue;
+            char text[32];
+            snprintf(text, sizeof(text), agent_idx == env->sdc_track_index ? "sdc" : "%d", agent_idx);
+            DrawText(text, sx - MeasureText(text, 20) / 2, sy, 20, PUFF_WHITE);
+        }
+    }
+}
 
-            if (screen_x >= 0 && screen_x <= client->width && screen_y >= 0 && screen_y <= client->height) {
-                char text[32];
-                snprintf(text, sizeof(text), "%d", womd_track_idx);
-                int text_width = MeasureText(text, 20);
-                DrawText(text, screen_x - text_width / 2, screen_y, 20, PUFF_WHITE);
+void draw_thick_line_3d(Vector3 a, Vector3 b, float thickness, Color color) {
+    float dx = b.x - a.x;
+    float dy = b.y - a.y;
+    float len = sqrtf(dx * dx + dy * dy);
+    if (len < 1e-6f)
+        return;
+    float nx = -dy / len * thickness * 0.5f;
+    float ny = dx / len * thickness * 0.5f;
+
+    Vector3 p0 = {a.x + nx, a.y + ny, a.z};
+    Vector3 p1 = {a.x - nx, a.y - ny, a.z};
+    Vector3 p2 = {b.x - nx, b.y - ny, b.z};
+    Vector3 p3 = {b.x + nx, b.y + ny, b.z};
+
+    DrawTriangle3D(p0, p1, p2, color);
+    DrawTriangle3D(p0, p2, p3, color);
+    DrawTriangle3D(p2, p1, p0, color);
+    DrawTriangle3D(p3, p2, p0, color);
+}
+
+void draw_sensor_noise_view(Drive *env, Client *client) {
+    int sdc_array_idx = 0;
+    if (env->sdc_track_index >= 0) {
+        for (int i = 0; i < env->active_agent_count; i++) {
+            if (env->active_agent_indices[i] == env->sdc_track_index) {
+                sdc_array_idx = i;
+                break;
             }
         }
     }
+
+    int ego_idx = env->active_agent_indices[sdc_array_idx];
+    Entity *ego = &env->entities[ego_idx];
+    if (ego->removed == 1)
+        return;
+
+    float cos_h = ego->heading_x;
+    float sin_h = ego->heading_y;
+    int num_samples = 5;
+
+    for (int j = 0; j < MAX_AGENTS; j++) {
+        int index = -1;
+        if (j < env->active_agent_count)
+            index = env->active_agent_indices[j];
+        else if (j < env->num_actors && env->static_agent_count > 0)
+            index = env->static_agent_indices[j - env->active_agent_count];
+        if (index == -1 || index == ego_idx)
+            continue;
+
+        Entity *other = &env->entities[index];
+        if (other->type > CYCLIST)
+            continue;
+        if (other->x == INVALID_POSITION)
+            continue;
+        if (other->removed == 1)
+            continue;
+
+        float dx = other->x - ego->x;
+        float dy = other->y - ego->y;
+        if ((dx * dx + dy * dy) > 2500.0f)
+            continue;
+
+        float gt_cos = other->heading_x;
+        float gt_sin = other->heading_y;
+        float gt_hl = other->length * 0.5f;
+        float gt_hw = other->width * 0.5f;
+
+        // Ground truth box (green)
+        Vector3 gt[4] = {
+            {other->x + gt_hl * gt_cos - gt_hw * gt_sin, other->y + gt_hl * gt_sin + gt_hw * gt_cos, Z_AGENTS},
+            {other->x + gt_hl * gt_cos + gt_hw * gt_sin, other->y + gt_hl * gt_sin - gt_hw * gt_cos, Z_AGENTS},
+            {other->x - gt_hl * gt_cos + gt_hw * gt_sin, other->y - gt_hl * gt_sin - gt_hw * gt_cos, Z_AGENTS},
+            {other->x - gt_hl * gt_cos - gt_hw * gt_sin, other->y - gt_hl * gt_sin + gt_hw * gt_cos, Z_AGENTS},
+        };
+        for (int k = 0; k < 4; k++)
+            draw_thick_line_3d(gt[k], gt[(k + 1) % 4], 0.45f, LIGHTGREEN);
+
+        // Noisy perception samples (purple)
+        for (int s = 0; s < num_samples; s++) {
+            float rel_x = dx * cos_h + dy * sin_h;
+            float rel_y = -dx * sin_h + dy * cos_h;
+            float noisy_rx = rel_x + gaussian_noise(env->obs_partner_noise_pos) / 0.02f;
+            float noisy_ry = rel_y + gaussian_noise(env->obs_partner_noise_pos) / 0.02f;
+            float px = ego->x + noisy_rx * cos_h - noisy_ry * sin_h;
+            float py = ego->y + noisy_rx * sin_h + noisy_ry * cos_h;
+
+            float p_hw =
+                (other->width / MAX_VEH_WIDTH + gaussian_noise(env->obs_partner_noise_pos)) * MAX_VEH_WIDTH * 0.5f;
+            float p_hl =
+                (other->length / MAX_VEH_LEN + gaussian_noise(env->obs_partner_noise_pos)) * MAX_VEH_LEN * 0.5f;
+
+            Vector3 pc[4] = {
+                {px + p_hl * gt_cos - p_hw * gt_sin, py + p_hl * gt_sin + p_hw * gt_cos, Z_AGENTS + 0.01f},
+                {px + p_hl * gt_cos + p_hw * gt_sin, py + p_hl * gt_sin - p_hw * gt_cos, Z_AGENTS + 0.01f},
+                {px - p_hl * gt_cos + p_hw * gt_sin, py - p_hl * gt_sin - p_hw * gt_cos, Z_AGENTS + 0.01f},
+                {px - p_hl * gt_cos - p_hw * gt_sin, py - p_hl * gt_sin + p_hw * gt_cos, Z_AGENTS + 0.01f},
+            };
+            Color sc = Fade(LIGHT_PURPLE, 0.6f);
+            for (int k = 0; k < 4; k++)
+                draw_thick_line_3d(pc[k], pc[(k + 1) % 4], 0.4f, sc);
+        }
+    }
+
+    // Ego box (cyan)
+    float hl = ego->length * 0.5f;
+    float hw = ego->width * 0.5f;
+    Vector3 ec[4] = {
+        {ego->x + hl * cos_h - hw * sin_h, ego->y + hl * sin_h + hw * cos_h, Z_AGENTS},
+        {ego->x + hl * cos_h + hw * sin_h, ego->y + hl * sin_h - hw * cos_h, Z_AGENTS},
+        {ego->x - hl * cos_h + hw * sin_h, ego->y - hl * sin_h - hw * cos_h, Z_AGENTS},
+        {ego->x - hl * cos_h - hw * sin_h, ego->y - hl * sin_h + hw * cos_h, Z_AGENTS},
+    };
+    for (int k = 0; k < 4; k++)
+        draw_thick_line_3d(ec[k], ec[(k + 1) % 4], 0.5f, DEEPBLUE);
+
+    // Ego heading arrow
+    Vector3 as = {ego->x, ego->y, Z_AGENTS};
+    Vector3 ae = {ego->x + cos_h * hl * 2.0f, ego->y + sin_h * hl * 2.0f, Z_AGENTS};
+    draw_thick_line_3d(as, ae, 0.25f, DEEPBLUE);
 }
 
 void c_render(Drive *env, int view_mode, int draw_traces) {
@@ -3005,8 +3641,16 @@ void c_render(Drive *env, int view_mode, int draw_traces) {
         env->client = make_client(env);
     }
 
+    if (env->client == NULL) {
+        return; // Silently skip rendering
+    }
+
     Client *client = env->client;
 
+    if (env->render_mode == RENDER_HEADLESS && client->recorder_pid <= 0) {
+        start_recorder(client, env->scenario_id, (int)client->width, (int)client->height);
+    }
+
     if (env->render_mode == RENDER_HEADLESS) { // Headless rendering via ffmpeg
         float map_width = env->grid_map->bottom_right_x - env->grid_map->top_left_x;
         float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y;
@@ -3014,13 +3658,22 @@ void c_render(Drive *env, int view_mode, int draw_traces) {
         Camera3D camera = {0};
 
         if (view_mode == VIEW_MODE_SIM_STATE) {
-            // Orthographic bird's-eye view over the entire map (fully observable)
-            camera.position = (Vector3){0.0, 0.0, 400.0f}; // Above the scene
-            camera.target = (Vector3){0.0, 0.0, 0.0};      // Look at origin
-            camera.up = (Vector3){0.0f, -1.0f, 0.0f};
-            camera.projection = CAMERA_ORTHOGRAPHIC;
-            camera.fovy = map_height;
-
+            if (env->sdc_track_index >= 0 && env->control_mode == CONTROL_SDC_ONLY) {
+                // Follow the SDC agent
+                Entity *sdc = &env->entities[env->sdc_track_index];
+                camera.position = (Vector3){sdc->x, sdc->y, 400.0f};
+                camera.target = (Vector3){sdc->x, sdc->y, 0.0f};
+                camera.up = (Vector3){0.0f, -1.0f, 0.0f};
+                camera.projection = CAMERA_ORTHOGRAPHIC;
+                camera.fovy = 100.0f;
+            } else {
+                // Orthographic bird's-eye view over the entire map (fully observable)
+                camera.position = (Vector3){0.0, 0.0, 400.0f};
+                camera.target = (Vector3){0.0, 0.0, 0.0};
+                camera.up = (Vector3){0.0f, -1.0f, 0.0f};
+                camera.projection = CAMERA_ORTHOGRAPHIC;
+                camera.fovy = map_height;
+            }
             BeginDrawing();
             ClearBackground(ROAD_COLOR);
             BeginMode3D(camera);
@@ -3046,12 +3699,19 @@ void c_render(Drive *env, int view_mode, int draw_traces) {
                     for (int t = env->init_steps; t < env->episode_length; t++) {
                         DrawSphere(
                             (Vector3){env->entities[idx].traj_x[t], env->entities[idx].traj_y[t], Z_AGENT_DETAILS},
-                            0.15f, EXPERT_REPLAY);
+                            0.15f, Fade(EXPERT_REPLAY, 0.5));
                     }
                 }
             }
 
             draw_scene(env, client, 1, 0, 0, 0);
+            DrawText(TextFormat("t=%d", env->timestep), 10, 10, 20, PUFF_WHITE);
+            for (int i = 0; i < env->active_agent_count; i++) {
+                int idx = env->active_agent_indices[i];
+                if (env->entities[idx].collision_state > 0) {
+                    DrawText(TextFormat("COLLISION agent %d at t=%d", idx, env->timestep), 10, 30, 20, PUFF_RED);
+                }
+            }
 
         } else if (view_mode == VIEW_MODE_BEV_AGENT_OBS) {
             // Orthographic bird's-eye view centered on the selected agent,
@@ -3071,6 +3731,51 @@ void c_render(Drive *env, int view_mode, int draw_traces) {
             BeginMode3D(camera);
             draw_scene(env, client, 1, 1, 0, 0);
 
+        } else if (view_mode == VIEW_MODE_SENSOR_NOISE) {
+            // Sensor noise visualization: BEV centered on SDC showing
+            // ground truth (green) vs noisy perception (purple)
+            int sdc_array_idx = 0;
+            if (env->sdc_track_index >= 0) {
+                for (int i = 0; i < env->active_agent_count; i++) {
+                    if (env->active_agent_indices[i] == env->sdc_track_index) {
+                        sdc_array_idx = i;
+                        break;
+                    }
+                }
+            }
+            int ego_idx = env->active_agent_indices[sdc_array_idx];
+            Entity *sdc = &env->entities[ego_idx];
+
+            camera.position = (Vector3){sdc->x, sdc->y, 400.0f};
+            camera.target = (Vector3){sdc->x, sdc->y, 0.0f};
+            camera.up = (Vector3){0.0f, -1.0f, 0.0f};
+            camera.projection = CAMERA_ORTHOGRAPHIC;
+            camera.fovy = 100.0f;
+
+            BeginDrawing();
+            ClearBackground(ROAD_COLOR);
+            BeginMode3D(camera);
+
+            // Draw roads first
+            for (int i = 0; i < env->num_entities; i++) {
+                if (env->entities[i].type < ROAD_LANE || env->entities[i].type > ROAD_EDGE)
+                    continue;
+                for (int j = 0; j < env->entities[i].array_size - 1; j++) {
+                    Vector3 start = {env->entities[i].traj_x[j], env->entities[i].traj_y[j], Z_ROAD_MARKINGS};
+                    Vector3 end = {env->entities[i].traj_x[j + 1], env->entities[i].traj_y[j + 1], Z_ROAD_MARKINGS};
+                    if (env->entities[i].type == ROAD_EDGE)
+                        draw_road_edge(env, start.x, start.y, end.x, end.y);
+                    else {
+                        Color c = (env->entities[i].type == ROAD_LANE) ? Fade(SOFT_YELLOW, 0.25f) : WHITE;
+                        rlSetLineWidth(2.0f);
+                        DrawLine3D(start, end, c);
+                    }
+                }
+            }
+
+            draw_sensor_noise_view(env, client);
+            EndMode3D();
+
         } else { // First-person perspective from a selected agent
             int agent_idx = env->active_agent_indices[env->human_agent_idx];
             Entity *agent = &env->entities[agent_idx];
@@ -3109,8 +3814,20 @@ void c_render(Drive *env, int view_mode, int draw_traces) {
             env->human_agent_idx = (env->human_agent_idx + 1) % env->active_agent_count;
         }
 
-        DrawText(TextFormat("Timestep: %d", env->timestep), 10, 50, 20, PUFF_WHITE);
-        DrawText(TextFormat("Controlling agent: %d", env->human_agent_idx), 10, 70, 20, PUFF_WHITE);
+        DrawText(TextFormat("Timestep: %d", env->timestep), 10, 65, 20, PUFF_WHITE);
+        DrawText(TextFormat("Controlling agent: %d \n", env->human_agent_idx), 10, 85, 20, PUFF_WHITE);
+        if (env->control_mode == CONTROL_REPLAY_LOGS) {
+            DrawText("Mode: Replay logs", 10, 20, 20, EXPERT_REPLAY);
+        } else if (env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) {
+            DrawText("Mode: Inferred expert actions", 10, 20, 20, LIGHT_PURPLE);
+            if (env->dynamics_model == DELTA_LOCAL) {
+                DrawText("Delta-local dynamics model", 10, 45, 20, LIGHT_PURPLE);
+            } else if (env->dynamics_model == CLASSIC) {
+                DrawText("Classic dynamics model", 10, 45, 20, LIGHT_PURPLE);
+            }
+            // DrawText(env->action_type == 1 ? "Actions: continuous" : "Actions: discrete", 10, 65, 20, LIGHT_PURPLE);
+        }
+
         int human_idx = env->active_agent_indices[env->human_agent_idx];
 
         Color action_color = IsKeyDown(KEY_LEFT_SHIFT) ? YELLOW : PUFF_WHITE;
@@ -3119,10 +3836,12 @@ void c_render(Drive *env, int view_mode, int draw_traces) {
             int *action_array = (int *)env->actions;
             int action_val = action_array[env->human_agent_idx];
 
-            if (env->dynamics_model == CLASSIC) {
-                int num_steer = 13;
-                int accel_idx = action_val / num_steer;
-                int steer_idx = action_val % num_steer;
+            bool can_take_control =
+                !(env->control_mode != CONTROL_REPLAY_LOGS || env->control_mode != CONTROL_INFERRED_EXPERT_ACTIONS);
+
+            if (env->dynamics_model == CLASSIC && can_take_control) {
+                int accel_idx = action_val / NUM_STEER_BINS;
+                int steer_idx = action_val % NUM_STEER_BINS;
                 float accel_value = ACCELERATION_VALUES[accel_idx];
                 float steer_value = STEERING_VALUES[steer_idx];
 
@@ -3147,15 +3866,15 @@ void c_render(Drive *env, int view_mode, int draw_traces) {
 
         int status_y = 150;
         if (IsKeyDown(KEY_LEFT_SHIFT)) {
-            DrawText("[shift pressed]", 10, status_y, 20, YELLOW);
+            DrawText("[shift pressed]", 10, status_y, 20, PUFF_WHITE);
             status_y += 20;
         }
         if (IsKeyDown(KEY_SPACE)) {
-            DrawText("[space pressed]", 10, status_y, 20, YELLOW);
+            DrawText("[space pressed]", 10, status_y, 20, PUFF_WHITE);
             status_y += 20;
         }
         if (IsKeyDown(KEY_LEFT_CONTROL)) {
-            DrawText("[ctrl pressed]", 10, status_y, 20, YELLOW);
+            DrawText("[ctrl pressed]", 10, status_y, 20, PUFF_WHITE);
             status_y += 20;
         }
 
@@ -3168,15 +3887,14 @@ void c_render(Drive *env, int view_mode, int draw_traces) {
 }
 
 void close_client(Client *client) {
-    if (client->recorder_pid > 0) {
-        close(client->recorder_pipefd[1]);
-        waitpid(client->recorder_pid, NULL, 0);
-    }
+    stop_recorder(client);
+
     for (int i = 0; i < 6; i++)
         UnloadModel(client->cars[i]);
     UnloadModel(client->cyclist);
     UnloadModel(client->pedestrian);
     CloseWindow();
+
     if (client->xvfb_pid > 0) {
         kill(client->xvfb_pid, SIGTERM);
         waitpid(client->xvfb_pid, NULL, 0);
diff --git a/pufferlib/ocean/drive/drive.py b/pufferlib/ocean/drive/drive.py
index 571b6dce28..46f716a980 100644
--- a/pufferlib/ocean/drive/drive.py
+++ b/pufferlib/ocean/drive/drive.py
@@ -3,7 +3,10 @@
 import json
 import struct
 import os
+
+import torch
 import pufferlib
+import math
 from enum import IntEnum
 from pufferlib.ocean.drive import binding
 from multiprocessing import Pool, cpu_count
@@ -14,6 +17,16 @@ class RenderView(IntEnum):
     FULL_SIM_STATE = 0  # Orthographic top-down, fully observable simulator state
     BEV_AGENT_OBS = 1  # Orthographic top-down, only show what the selected agent can observe
     AGENT_PERSP = 2  # Third-person perspective following selected agent
+    SENSOR_NOISE = 3  # Agent view, including perception noise
+
+
+class RegMode(IntEnum):
+    NONE = 0
+    LOG_PROB_DIRECT = 1
+    KL_ANCHOR = 2
+
+
+DYNAMICS_MODEL_MAP = {"classic": 0, "jerk": 1, "delta_local": 2}
 
 
 class Drive(pufferlib.PufferEnv):
@@ -24,10 +37,9 @@ def __init__(
         width=1280,
         height=1024,
         human_agent_idx=0,
-        reward_vehicle_collision=-0.1,
-        reward_offroad_collision=-0.1,
+        reward_vehicle_collision=-0.5,
+        reward_offroad_collision=-0.5,
         reward_goal=1.0,
-        reward_goal_post_respawn=0.5,
         goal_behavior=0,
         goal_target_distance=10.0,
         goal_radius=2.0,
@@ -49,6 +61,17 @@ def __init__(
         control_mode="control_vehicles",
         max_controlled_agents=32,
         map_dir="resources/drive/binaries/training",
+        ini_file_path="pufferlib/config/ocean/drive.ini",
+        reg_mode="None",
+        anchor_cpt_path=None,
+        uses_memory=False,
+        memory_size=0,
+        fix_rewards=False,
+        fix_lambdas=True,
+        lambda_value=0.0,
+        obs_partner_noise_speed=0.0,
+        obs_partner_noise_pos=0.0,
+        async_resets=True,
     ):
         # env
         self.dt = dt
@@ -58,7 +81,6 @@ def __init__(
         self.reward_vehicle_collision = reward_vehicle_collision
         self.reward_offroad_collision = reward_offroad_collision
         self.reward_goal = reward_goal
-        self.reward_goal_post_respawn = reward_goal_post_respawn
         self.goal_radius = goal_radius
         self.goal_speed = goal_speed
         self.goal_behavior = goal_behavior
@@ -70,12 +92,22 @@ def __init__(
         self.termination_mode = termination_mode
         self.resample_frequency = resample_frequency
         self.dynamics_model = dynamics_model
+        self.ini_file_path = ini_file_path
+        self.uses_memory = uses_memory
+        self.memory_size = memory_size
+        self.total_num_samples = 0
         self.max_controlled_agents = max_controlled_agents
+        self.anchor_cpt_path = anchor_cpt_path
+        self.fix_rewards = fix_rewards
+        self.fix_lambdas = fix_lambdas
+        self.lambda_value = lambda_value
+        self.obs_partner_noise_speed = obs_partner_noise_speed
+        self.obs_partner_noise_pos = obs_partner_noise_pos
+        self._dynamics_model_flag = DYNAMICS_MODEL_MAP[dynamics_model]
+        self.async_resets = bool(async_resets)
 
         # Observation space calculation
-        self.ego_features = {"classic": binding.EGO_FEATURES_CLASSIC, "jerk": binding.EGO_FEATURES_JERK}.get(
-            dynamics_model
-        )
+        self.ego_features = binding.EGO_FEATURES_JERK if dynamics_model == "jerk" else binding.EGO_FEATURES
 
         # Extract observation shapes from constants
         # These need to be defined in C, since they determine the shape of the arrays
@@ -95,6 +127,12 @@ def __init__(
         self.init_mode_str = init_mode
         self.control_mode_str = control_mode
         self.map_dir = map_dir
+        str_to_reg_mode = {
+            "None": RegMode.NONE,
+            "log_prob_direct": RegMode.LOG_PROB_DIRECT,
+            "kl_anchor": RegMode.KL_ANCHOR,
+        }
+        self.reg_mode = str_to_reg_mode.get(str(reg_mode).strip('"'))
 
         if self.control_mode_str == "control_vehicles":
             self.control_mode = 0
@@ -106,6 +144,10 @@ def __init__(
             self.control_mode = 3
         elif self.control_mode_str == "control_mixed_play":
             self.control_mode = 4
+        elif self.control_mode_str == "inferred_expert_actions":
+            self.control_mode = 5
+        elif self.control_mode_str == "expert_replay":
+            self.control_mode = 6
         else:
             raise ValueError(
                 f"control_mode must be one of 'control_vehicles', 'control_wosac', 'control_agents' or 'control_mixed_play'. Got: {self.control_mode_str}"
@@ -122,14 +164,19 @@ def __init__(
         if action_type == "discrete":
             if dynamics_model == "classic":
                 # Joint action space (assume dependence)
-                self.single_action_space = gymnasium.spaces.MultiDiscrete([7 * 13])
+                self.joint_action_space_size = binding.NUM_ACCEL_BINS * binding.NUM_STEER_BINS
+                self.single_action_space = gymnasium.spaces.MultiDiscrete([self.joint_action_space_size])
                 # Multi discrete (assume independence)
                 # self.single_action_space = gymnasium.spaces.MultiDiscrete([7, 13])
+            elif dynamics_model == "delta_local":
+                self.single_action_space = gymnasium.spaces.MultiDiscrete(
+                    [binding.NUM_DX_BINS, binding.NUM_DY_BINS, binding.NUM_YAW_BINS]
+                )
             elif dynamics_model == "jerk":
                 # Joint action space (assume dependence) - 4 longitudinal × 3 lateral = 12
                 self.single_action_space = gymnasium.spaces.MultiDiscrete([4 * 3])
             else:
-                raise ValueError(f"dynamics_model must be 'classic' or 'jerk'. Got: {dynamics_model}")
+                raise ValueError(f"dynamics_model must be 'classic', 'delta_local' or 'jerk'. Got: {dynamics_model}")
         elif action_type == "continuous":
             self.single_action_space = gymnasium.spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
         else:
@@ -153,6 +200,7 @@ def __init__(
 
         # Iterate through all maps to count total agents that can be initialized for each map
         agent_offsets, map_ids, num_envs = binding.shared(
+            seed=seed,
             map_dir=map_dir,
             num_agents=num_agents,
             num_maps=num_maps,
@@ -185,7 +233,8 @@ def __init__(
                 reward_vehicle_collision=reward_vehicle_collision,
                 reward_offroad_collision=reward_offroad_collision,
                 reward_goal=reward_goal,
-                reward_goal_post_respawn=reward_goal_post_respawn,
+                obs_partner_noise_pos=self.obs_partner_noise_pos,
+                obs_partner_noise_speed=self.obs_partner_noise_speed,
                 goal_radius=goal_radius,
                 goal_speed=goal_speed,
                 goal_behavior=self.goal_behavior,
@@ -197,18 +246,40 @@ def __init__(
                 termination_mode=(int(self.termination_mode) if self.termination_mode is not None else 0),
                 map_id=map_ids[i],
                 max_agents=nxt - cur,
-                ini_file="pufferlib/config/ocean/drive.ini",
+                ini_file=self.ini_file_path,
                 init_steps=init_steps,
                 init_mode=self.init_mode,
                 control_mode=self.control_mode,
                 map_dir=map_dir,
                 max_controlled_agents=self.max_controlled_agents,
                 render_mode=render_mode,
+                lambda_value=lambda_value,
+                fix_lambdas=self.fix_lambdas,
+                fix_reward=self.fix_rewards,
+                async_resets=self.async_resets,
+                dynamics_model=self._dynamics_model_flag,
             )
             self.env_ids.append(env_id)
 
+        # Approximation to check if we should ever resample or not
+        self.needs_resampling = self.num_maps > (self.num_agents / 2)
+        self._resample_count = 0
+        self.seed = seed
+
         self.c_envs = binding.vectorize(*self.env_ids)
 
+        # Per-agent lambda value for conditioning
+        self.lambda_obs_idx = binding.LAMBDA_CONDITIONING_IDX
+        self.reward_veh_obs_idx = binding.REWARD_COLLISION_IDX
+        self.reward_offroad_obs_idx = binding.REWARD_OFFROAD_COLLISION_IDX
+        self.reward_goal_obs_idx = binding.REWARD_GOAL_IDX
+
+        # Per-agent lambda value for conditioning
+        self.lambda_obs_idx = binding.LAMBDA_CONDITIONING_IDX
+        self.reward_veh_obs_idx = binding.REWARD_COLLISION_IDX
+        self.reward_offroad_obs_idx = binding.REWARD_OFFROAD_COLLISION_IDX
+        self.reward_goal_obs_idx = binding.REWARD_GOAL_IDX
+
     def reset(self, seed=0):
         binding.vec_reset(self.c_envs, seed)
         self.tick = 0
@@ -219,7 +290,9 @@ def resample_maps(self):
         """Resample environment maps."""
         self.tick = 0
         binding.vec_close(self.c_envs)
+        resample_seed = self.seed + self._resample_count
         agent_offsets, map_ids, num_envs = binding.shared(
+            seed=resample_seed,
             num_agents=self.num_agents,
             num_maps=self.num_maps,
             init_mode=self.init_mode,
@@ -235,7 +308,6 @@ def resample_maps(self):
         self.map_ids = map_ids
         self.num_envs = num_envs
         self.env_ids = []
-        seed = np.random.randint(0, 2**32 - 1)
         for i in range(num_envs):
             cur = agent_offsets[i]
             nxt = agent_offsets[i + 1]
@@ -245,13 +317,12 @@ def resample_maps(self):
                 self.rewards[cur:nxt],
                 self.terminals[cur:nxt],
                 self.truncations[cur:nxt],
-                seed,
+                resample_seed,
                 action_type=self._action_type_flag,
                 human_agent_idx=self.human_agent_idx,
                 reward_vehicle_collision=self.reward_vehicle_collision,
                 reward_offroad_collision=self.reward_offroad_collision,
                 reward_goal=self.reward_goal,
-                reward_goal_post_respawn=self.reward_goal_post_respawn,
                 goal_radius=self.goal_radius,
                 goal_behavior=self.goal_behavior,
                 goal_target_distance=self.goal_target_distance,
@@ -262,7 +333,7 @@ def resample_maps(self):
                 episode_length=(int(self.episode_length) if self.episode_length is not None else None),
                 map_id=map_ids[i],
                 max_agents=nxt - cur,
-                ini_file="pufferlib/config/ocean/drive.ini",
+                ini_file=self.ini_file_path,
                 init_steps=self.init_steps,
                 init_mode=self.init_mode,
                 control_mode=self.control_mode,
@@ -270,19 +341,28 @@ def resample_maps(self):
                 termination_mode=(int(self.termination_mode) if self.termination_mode is not None else 0),
                 max_controlled_agents=self.max_controlled_agents,
                 render_mode=self.render_mode,
+                lambda_value=self.lambda_value,
+                fix_rewards=int(self.fix_rewards),
+                fix_lambdas=int(self.fix_lambdas),
+                dynamics_model=self._dynamics_model_flag,
+                obs_partner_noise_pos=self.obs_partner_noise_pos,
+                obs_partner_noise_speed=self.obs_partner_noise_speed,
+                async_resets=int(self.async_resets),
             )
             self.env_ids.append(env_id)
         self.c_envs = binding.vectorize(*self.env_ids)
+        self._resample_count += 1
 
-        binding.vec_reset(self.c_envs, seed)
-        self.terminals[:] = 1
+        binding.vec_reset(self.c_envs, resample_seed)
         self.truncations[:] = 1
+        self.terminals[:] = 1
 
     def step(self, actions, per_env_logs=False):
         self.terminals[:] = 0
         self.truncations[:] = 0
         self.actions[:] = actions
         binding.vec_step(self.c_envs)
+
         self.tick += 1
         info = []
         if self.tick % self.report_interval == 0:
@@ -295,8 +375,10 @@ def step(self, actions, per_env_logs=False):
                 if log:
                     info.append(log)
 
-        if self.tick > 0 and self.resample_frequency > 0 and self.tick % self.resample_frequency == 0:
-            self.resample_maps()
+        if self.needs_resampling:
+            if self.tick > 0 and self.resample_frequency > 0 and self.tick % self.resample_frequency == 0:
+                # Resample batch of scenes used for training
+                self.resample_maps()
 
         return (self.observations, self.rewards, self.terminals, self.truncations, info)
 
@@ -372,6 +454,97 @@ def get_ground_truth_trajectories(self):
 
         return trajectories
 
+    def _hash_pair(self, obs, act):
+        return hash((obs.round(3).tobytes(), act.round(2).tobytes()))
+
+    def _init_regularization_strategy(self, device="cuda"):
+        bc_anchor = None
+        data = {}
+
+        if self.reg_mode == RegMode.KL_ANCHOR:
+            from examples.train_bc_policy import BCPolicy
+
+            if self.dynamics_model == "delta_local":
+                output_sizes = [binding.NUM_DX_BINS, binding.NUM_DY_BINS, binding.NUM_YAW_BINS]
+            else:
+                output_sizes = [self.joint_action_space_size]
+
+            bc_policy = BCPolicy(
+                obs_dim=self.num_obs,
+                input_size=128,
+                max_partner_objects=self.max_partner_objects,
+                partner_features=self.partner_features,
+                max_road_objects=self.max_road_objects,
+                road_features=self.road_features,
+                ego_dim=self.ego_features,
+                hidden_size=512,
+                output_sizes=output_sizes,
+            ).to(device)
+
+            if self.anchor_cpt_path is None:
+                self.anchor_cpt_path = f"models/bc_{self.dynamics_model}_{self.num_maps}.pt"
+
+            bc_policy.load_state_dict(torch.load(self.anchor_cpt_path, map_location=device))
+            bc_policy.eval()
+            for p in bc_policy.parameters():
+                p.requires_grad = False
+            bc_anchor = bc_policy
+            print(f"Loaded BC anchor policy from {self.anchor_cpt_path}")
+
+        elif self.reg_mode == RegMode.LOG_PROB_DIRECT:
+            total_samples, unique_samples = self._prepare_human_data()
+            data["data/total_human_samples"] = total_samples
+            data["data/unique_human_samples"] = unique_samples
+            data["data/perc_unique_human_samples"] = (unique_samples / total_samples) * 100
+            print(f"Prepared {total_samples} human demonstrations ({unique_samples} unique)")
+
+        return bc_anchor, data
+
+    def _prepare_human_data(self, max_samples=16_384):
+        """Prepare human demonstrations."""
+        trajectory_length = 91
+
+        if self.dynamics_model == "delta_local":
+            discrete_action_dim = 3
+        else:  # Classic dynamics model
+            discrete_action_dim = 1
+
+        expert_actions_discrete = np.full(
+            (trajectory_length, self.num_agents, discrete_action_dim), -1.0, dtype=np.float32
+        )
+        expert_observations_full = np.full((trajectory_length, self.num_agents, self.num_obs), -1.0, dtype=np.float32)
+
+        binding.vec_collect_expert_data(self.c_envs, expert_actions_discrete, expert_observations_full)
+
+        if np.all(expert_actions_discrete == -1):
+            raise ValueError("No valid human demonstrations could be collected. Please check the data format.")
+
+        if self.dynamics_model == "delta_local":
+            # Any dimension being -1 means the timestep is invalid
+            invalid_action_mask = expert_actions_discrete[:, :, 0] == -1.0
+        else:
+            invalid_action_mask = (expert_actions_discrete == -1.0).squeeze(-1)
+
+        self.expert_actions_discrete = torch.Tensor(expert_actions_discrete[~invalid_action_mask])
+        self.expert_observations_full = torch.Tensor(expert_observations_full[~invalid_action_mask])
+
+        # Count unique number of (observation, action) pairs. This gives
+        # an idea of the diversity and coverage of the human demonstrations.
+        obs_np = self.expert_observations_full.numpy()
+        act_np = self.expert_actions_discrete.numpy()
+        self.total_unique_samples = len({self._hash_pair(obs_np[i], act_np[i]) for i in range(len(obs_np))})
+        self.total_num_samples = self.expert_actions_discrete.shape[0]
+
+        return self.total_num_samples, self.total_unique_samples
+
+    def sample_human_demonstrations(self, batch_size=512):
+        # get random indices between min and max
+        rand_idx = torch.randint(0, self.total_num_samples - 1, (batch_size,))
+        return (
+            self.expert_actions_discrete[rand_idx],
+            self.expert_observations_full[rand_idx],
+        )
+
     def get_road_edge_polylines(self):
         """Get road edge polylines for all scenarios.
 
@@ -400,8 +573,12 @@ def get_road_edge_polylines(self):
 
         return polylines
 
-    def render(self, view_mode: RenderView = RenderView.FULL_SIM_STATE, draw_traces: bool = True, env_id: int = 0):
-        binding.vec_render(self.c_envs, int(view_mode), draw_traces, env_id)
+    def stop_recorder(self, env_idx: int = 0):
+        """Stop the ffmpeg recorder for the given env (flush video, no new recorder)."""
+        binding.vec_stop_recorder(self.c_envs, env_idx)
+
+    def render(self, view_mode: RenderView = RenderView.FULL_SIM_STATE, draw_traces: bool = True, env_idx: int = 0):
+        binding.vec_render(self.c_envs, int(view_mode), draw_traces, env_idx)
 
     def close(self):
         binding.vec_close(self.c_envs)
@@ -421,6 +598,137 @@ def scenario_ids(self) -> list[str]:
         return [s.rstrip("\x00") for s in binding.vec_get_scenario_ids(self.c_envs)]
 
 
+def infer_human_actions(obj):
+    """Infer expert actions using inverse bicycle model and delta-local displacements.
+
+    Returns:
+        (expert_acceleration, expert_steering,
+         expert_delta_x, expert_delta_y, expert_delta_yaw)
+        Each is a list of length trajectory_length (91).
+        -1.0 is the placeholder for invalid timesteps (accel/steering).
+        0.0 is used for invalid delta timesteps.
+    """
+    trajectory_length = 91
+
+    expert_acceleration = []
+    expert_steering = []
+    expert_delta_x = []
+    expert_delta_y = []
+    expert_delta_yaw = []
+
+    positions = obj.get("position", [])
+    velocities = obj.get("velocity", [])
+    headings = obj.get("heading", [])
+    valids = obj.get("valid", [])
+
+    if len(positions) < 2 or len(velocities) < 2 or len(headings) < 2:
+        return (
+            [-1.0] * trajectory_length,
+            [-1.0] * trajectory_length,
+            [0.0] * trajectory_length,
+            [0.0] * trajectory_length,
+            [0.0] * trajectory_length,
+        )
+
+    dt = 0.1
+    vehicle_length = obj.get("length", 4.5)
+    wheelbase = 1.0 * vehicle_length
+
+    for t in range(trajectory_length):
+        # Check validity for both current and next timestep
+        valid_pair = (
+            t < len(positions)
+            and t < len(velocities)
+            and t < len(headings)
+            and t < len(valids)
+            and valids[t]
+            and t + 1 < len(positions)
+            and t + 1 < len(velocities)
+            and t + 1 < len(headings)
+            and t + 1 < len(valids)
+            and valids[t + 1]
+        )
+
+        if not valid_pair:
+            expert_acceleration.append(-1.0)
+            expert_steering.append(-1.0)
+            expert_delta_x.append(0.0)
+            expert_delta_y.append(0.0)
+            expert_delta_yaw.append(0.0)
+            continue
+
+        # Current and next state
+        pos_t = positions[t]
+        pos_t1 = positions[t + 1]
+        vel_t = velocities[t]
+        vel_t1 = velocities[t + 1]
+        heading_t = headings[t]
+        heading_t1 = headings[t + 1]
+
+        speed_t = math.sqrt(vel_t.get("x", 0.0) ** 2 + vel_t.get("y", 0.0) ** 2)
+        speed_t1 = math.sqrt(vel_t1.get("x", 0.0) ** 2 + vel_t1.get("y", 0.0) ** 2)
+
+        # Classic inverse bicycle model (accel + steering)
+        acceleration = (speed_t1 - speed_t) / dt
+
+        heading_diff = heading_t1 - heading_t
+        while heading_diff > math.pi:
+            heading_diff -= 2 * math.pi
+        while heading_diff < -math.pi:
+            heading_diff += 2 * math.pi
+
+        yaw_rate = heading_diff / dt
+
+        steering = 0.0
+        if speed_t > 0.1:
+            tan_steering = (yaw_rate * wheelbase) / speed_t
+            tan_steering = max(-10.0, min(10.0, tan_steering))
+            steering = math.atan(tan_steering)
+
+        acceleration = max(-20.0, min(20.0, acceleration))
+        steering = max(-4.0, min(4.0, steering))
+
+        expert_acceleration.append(acceleration)
+        expert_steering.append(steering)
+
+        # Delta-local: (dx, dy, dyaw) in agent's local frame
+        global_dx = pos_t1.get("x", 0.0) - pos_t.get("x", 0.0)
+        global_dy = pos_t1.get("y", 0.0) - pos_t.get("y", 0.0)
+
+        # Rotate global displacement into agent's local frame at time t
+        cos_h = math.cos(heading_t)
+        sin_h = math.sin(heading_t)
+        local_dx = cos_h * global_dx + sin_h * global_dy
+        local_dy = -sin_h * global_dx + cos_h * global_dy
+
+        # Clip to bounds
+        local_dx = max(-6.0, min(6.0, local_dx))
+        local_dy = max(-6.0, min(6.0, local_dy))
+
+        expert_delta_x.append(local_dx)
+        expert_delta_y.append(local_dy)
+        expert_delta_yaw.append(heading_diff)  # already wrapped above
+
+    # Pad/truncate to exact trajectory_length
+    for arr, pad_val in [
+        (expert_acceleration, -1.0),
+        (expert_steering, -1.0),
+        (expert_delta_x, 0.0),
+        (expert_delta_y, 0.0),
+        (expert_delta_yaw, 0.0),
+    ]:
+        while len(arr) < trajectory_length:
+            arr.append(pad_val)
+
+    expert_acceleration = expert_acceleration[:trajectory_length]
+    expert_steering = expert_steering[:trajectory_length]
+    expert_delta_x = expert_delta_x[:trajectory_length]
+    expert_delta_y = expert_delta_y[:trajectory_length]
+    expert_delta_yaw = expert_delta_yaw[:trajectory_length]
+
+    return expert_acceleration, expert_steering, expert_delta_x, expert_delta_y, expert_delta_yaw
+
+
 def calculate_area(p1, p2, p3):
     # Calculate the area of the triangle using the determinant method
     return 0.5 * abs((p1["x"] - p3["x"]) * (p2["y"] - p1["y"]) - (p1["x"] - p2["x"]) * (p3["y"] - p1["y"]))
@@ -552,6 +860,19 @@ def save_map_binary(map_data, output_file, unique_map_id):
                 )
             )
 
+            # Infer and write human actions
+            if obj_type in [1, 2, 3]:
+                human_accel, human_steering, human_dx, human_dy, human_dyaw = infer_human_actions(obj)
+                f.write(struct.pack(f"{trajectory_length}f", *human_accel))
+                f.write(struct.pack(f"{trajectory_length}f", *human_steering))
+                f.write(struct.pack(f"{trajectory_length}f", *human_dx))
+                f.write(struct.pack(f"{trajectory_length}f", *human_dy))
+                f.write(struct.pack(f"{trajectory_length}f", *human_dyaw))
+            else:
+                # accel, steering, delta_x, delta_y, delta_yaw
+                for _ in range(5):
+                    f.write(struct.pack(f"{trajectory_length}f", *[0.0] * trajectory_length))
+
             # Write scalar fields
             f.write(struct.pack("f", float(obj.get("width", 0.0))))
             f.write(struct.pack("f", float(obj.get("length", 0.0))))
@@ -684,7 +1005,7 @@ def process_all_maps(
                 print(f"  {name}: {error}")
 
 
-def test_performance(timeout=10, atn_cache=1024, num_agents=1024):
+def test_performance(timeout=10, atn_cache=12, num_agents=12):
     import time
 
     env = Drive(
@@ -692,8 +1013,12 @@ def test_performance(timeout=10, atn_cache=1024, num_agents=1024):
         num_maps=1,
         control_mode="control_vehicles",
         init_mode="create_all_valid",
+        map_dir="resources/drive/binaries/interactive_data_training_100",
         init_steps=0,
         episode_length=91,
+        fix_lambdas=True,
+        fix_rewards=False,
+        lambda_value=0.0,
     )
 
     env.reset()
@@ -706,8 +1031,16 @@ def test_performance(timeout=10, atn_cache=1024, num_agents=1024):
     start = time.time()
     while time.time() - start < timeout:
         atn = actions[tick % atn_cache]
-        env.step(atn)
+        obs, rewards, terminals, truncated, info_list = env.step(atn)
+        # print(obs[:, 0])
+        # print(obs[:, 1])
+        # print(obs[:, 2])
+        # print(obs[:, 3])
         tick += 1
+        # print(tick)
+
+        if tick > 4:
+            break
 
     print(f"SPS: {num_agents * tick / (time.time() - start)}")
 
@@ -717,7 +1050,7 @@ def test_performance(timeout=10, atn_cache=1024, num_agents=1024):
 if __name__ == "__main__":
     # test_performance()
     # Process the train dataset
-    process_all_maps(data_folder="data/processed/training")
+    process_all_maps(data_folder="data/processed/interactive_data_validation")
     # Process the validation/test dataset
     # process_all_maps(data_folder="data/processed/validation")
     # # Process the validation_interactive dataset
diff --git a/pufferlib/ocean/drive/drivenet.h b/pufferlib/ocean/drive/drivenet.h
index 5719081bb1..7788c64467 100644
--- a/pufferlib/ocean/drive/drivenet.h
+++ b/pufferlib/ocean/drive/drivenet.h
@@ -47,7 +47,7 @@ struct DriveNet {
     GELU *gelu;
     Linear *shared_embedding;
     ReLU *relu;
-    LSTM *lstm;
+    // LSTM *lstm;
     Linear *actor;
     Linear *value_fn;
     Multidiscrete *multidiscrete;
@@ -56,7 +56,7 @@ struct DriveNet {
 DriveNet *init_drivenet(Weights *weights, int num_agents, int dynamics_model) {
     DriveNet *net = calloc(1, sizeof(DriveNet));
     // Use constants directly from drive.h
-    int ego_dim = (dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC;
+    int ego_dim = (dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES;
     int max_partners = MAX_AGENTS - 1;
     int max_road_obs = MAX_ROAD_SEGMENT_OBSERVATIONS;
     int partner_features = PARTNER_FEATURES;
@@ -69,8 +69,8 @@ DriveNet *init_drivenet(Weights *weights, int num_agents, int dynamics_model) {
     int action_size, logit_sizes[2];
     int action_dim;
     if (dynamics_model == CLASSIC) {
-        action_size = 7 * 13; // Joint action space
-        logit_sizes[0] = 7 * 13;
+        action_size = NUM_ACCEL_BINS * NUM_STEER_BINS; // Joint action space
+        logit_sizes[0] = NUM_ACCEL_BINS * NUM_STEER_BINS;
         action_dim = 1;
     } else {                 // JERK
         action_size = 4 * 3; // Joint action space (4 longitudinal × 3 lateral = 12)
@@ -108,9 +108,9 @@ DriveNet *init_drivenet(Weights *weights, int num_agents, int dynamics_model) {
     net->relu = make_relu(num_agents, hidden_size);
     net->actor = make_linear(weights, num_agents, hidden_size, action_size);
     net->value_fn = make_linear(weights, num_agents, hidden_size, 1);
-    net->lstm = make_lstm(weights, num_agents, hidden_size, NN_HIDDEN_SIZE);
-    memset(net->lstm->state_h, 0, num_agents * NN_HIDDEN_SIZE * sizeof(float));
-    memset(net->lstm->state_c, 0, num_agents * NN_HIDDEN_SIZE * sizeof(float));
+    // net->lstm = make_lstm(weights, num_agents, hidden_size, NN_HIDDEN_SIZE);
+    // memset(net->lstm->state_h, 0, num_agents * NN_HIDDEN_SIZE * sizeof(float));
+    // memset(net->lstm->state_c, 0, num_agents * NN_HIDDEN_SIZE * sizeof(float));
     net->multidiscrete = make_multidiscrete(num_agents, logit_sizes, action_dim);
     return net;
 }
@@ -144,7 +144,7 @@ void free_drivenet(DriveNet *net) {
     free(net->multidiscrete);
     free(net->actor);
     free(net->value_fn);
-    free(net->lstm);
+    // free(net->lstm);
     free(net);
 }
 
@@ -269,9 +269,9 @@ void forward(DriveNet *net, float *observations, int *actions) {
     gelu(net->gelu, net->cat2->output);
     linear(net->shared_embedding, net->gelu->output);
     relu(net->relu, net->shared_embedding->output);
-    lstm(net->lstm, net->relu->output);
-    linear(net->actor, net->lstm->state_h);
-    linear(net->value_fn, net->lstm->state_h);
+    // lstm(net->lstm, net->relu->output);
+    linear(net->actor, net->relu->output);
+    linear(net->value_fn, net->relu->output);
 
     // Get action by taking argmax of actor output
     softmax_multidiscrete(net->multidiscrete, net->actor->output, actions);
diff --git a/pufferlib/ocean/env_binding.h b/pufferlib/ocean/env_binding.h
index c2c0c10f03..34680b34ed 100644
--- a/pufferlib/ocean/env_binding.h
+++ b/pufferlib/ocean/env_binding.h
@@ -660,6 +660,7 @@ static PyObject *env_log(PyObject *self, PyObject *args) {
     int num_keys = sizeof(Log) / sizeof(float);
     for (int j = 0; j < num_keys; j++) {
         ((float *)&aggregate)[j] += ((float *)&env->log)[j];
+        ((float *)&env->log)[j] = 0.0f; // Zero out after consuming it
     }
 
     PyObject *dict = PyDict_New();
@@ -1036,6 +1037,111 @@ static char *unpack_str(PyObject *kwargs, char *key) {
     return ret;
 }
 
+static PyObject *vec_collect_expert_data(PyObject *self, PyObject *args) {
+    if (PyTuple_Size(args) != 3) {
+        PyErr_SetString(PyExc_TypeError, "collect_expert_data requires 3 arguments");
+        return NULL;
+    }
+
+    VecEnv *vec = unpack_vecenv(args);
+    if (!vec) {
+        return NULL;
+    }
+
+    // Discrete actions
+    PyObject *discrete_actions_arg = PyTuple_GetItem(args, 1);
+    if (!PyObject_TypeCheck(discrete_actions_arg, &PyArray_Type)) {
+        PyErr_SetString(PyExc_TypeError, "expert_actions_discrete must be a NumPy array");
+        return NULL;
+    }
+    PyArrayObject *expert_actions_discrete = (PyArrayObject *)discrete_actions_arg;
+
+    // Observations
+    PyObject *obs_arg = PyTuple_GetItem(args, 2);
+    if (!PyObject_TypeCheck(obs_arg, &PyArray_Type)) {
+        PyErr_SetString(PyExc_TypeError, "expert_observations must be a NumPy array");
+        return NULL;
+    }
+    PyArrayObject *expert_observations = (PyArrayObject *)obs_arg;
+
+    // Check array shapes
+    if (PyArray_NDIM(expert_actions_discrete) != 3) {
+        PyErr_SetString(PyExc_ValueError, "expert_actions_discrete must be 3D (trajectory_length, num_agents, 1)");
+        return NULL;
+    }
+    if (PyArray_NDIM(expert_observations) != 3) {
+        PyErr_SetString(PyExc_ValueError, "expert_observations must be 3D (trajectory_length, num_agents, obs_dim)");
+        return NULL;
+    }
+
+    int trajectory_length = PyArray_DIM(expert_actions_discrete, 0);
+    int max_obs = EGO_FEATURES + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS;
+
+    // Process data for each vectorized environment
+    int agent_offset = 0;
+    for (int i = 0; i < vec->num_envs; i++) {
+        Env *env = vec->envs[i];
+        int num_agents = env->active_agent_count;
+        int discrete_action_dim = (env->dynamics_model == DELTA_LOCAL) ? 3 : 1;
+
+        float *env_actions_discrete =
+            (float *)malloc(trajectory_length * num_agents * discrete_action_dim * sizeof(float));
+        float *env_obs = (float *)malloc(trajectory_length * num_agents * max_obs * sizeof(float));
+
+        if (!env_actions_discrete || !env_obs) {
+            PyErr_SetString(PyExc_MemoryError, "Failed to allocate temporary buffers");
+            free(env_actions_discrete);
+            free(env_obs);
+            return NULL;
+        }
+
+        c_collect_expert_data(env, env_actions_discrete, env_obs);
+
+        // Copy results back to Python arrays
+        for (int t = 0; t < trajectory_length; t++) {
+            for (int a = 0; a < num_agents; a++) {
+                // Copy discrete actions (1 value for classic, 3 for delta_local)
+                float *discrete_action_src =
+                    &env_actions_discrete[t * num_agents * discrete_action_dim + a * discrete_action_dim];
+                float *discrete_action_dst = (float *)PyArray_GETPTR3(expert_actions_discrete, t, agent_offset + a, 0);
+                for (int d = 0; d < discrete_action_dim; d++) {
+                    discrete_action_dst[d] = discrete_action_src[d];
+                }
+
+                // Copy observations
+                float *obs_src = &env_obs[t * num_agents * max_obs + a * max_obs];
+                float *obs_dst = (float *)PyArray_GETPTR3(expert_observations, t, agent_offset + a, 0);
+                memcpy(obs_dst, obs_src, max_obs * sizeof(float));
+            }
+        }
+
+        free(env_actions_discrete);
+        free(env_obs);
+        agent_offset += num_agents;
+    }
+
+    Py_RETURN_NONE;
+}
+
+static PyObject *vec_stop_recorder(PyObject *self, PyObject *args) {
+    if (PyTuple_Size(args) != 2) {
+        PyErr_SetString(PyExc_TypeError, "vec_stop_recorder requires 2 arguments");
+        return NULL;
+    }
+    VecEnv *vec = unpack_vecenv(args);
+    if (!vec)
+        return NULL;
+    int env_idx = PyLong_AsLong(PyTuple_GetItem(args, 1));
+    if (env_idx < 0 || env_idx >= vec->num_envs) {
+        PyErr_SetString(PyExc_IndexError, "env_idx out of range");
+        return NULL;
+    }
+    Drive *drive = (Drive *)vec->envs[env_idx];
+    if (drive->client != NULL)
+        stop_recorder(drive->client);
+    Py_RETURN_NONE;
+}
+
 // Method table
 static PyMethodDef methods[] = {
     {"env_init", (PyCFunction)env_init, METH_VARARGS | METH_KEYWORDS,
@@ -1056,6 +1162,7 @@ static PyMethodDef methods[] = {
     {"vec_close", vec_close, METH_VARARGS, "Close the vector of environments"},
     {"vec_get_scenario_ids", vec_get_scenario_ids, METH_VARARGS, "Get scenario IDs for all envs"},
     {"shared", (PyCFunction)my_shared, METH_VARARGS | METH_KEYWORDS, "Shared state"},
+    {"vec_collect_expert_data", vec_collect_expert_data, METH_VARARGS, "Collect expert actions and observations"},
     {"get_global_agent_state", get_global_agent_state, METH_VARARGS, "Get global agent state"},
     {"vec_get_global_agent_state", vec_get_global_agent_state, METH_VARARGS, "Get agent state from vectorized env"},
     {"get_ground_truth_trajectories", get_ground_truth_trajectories, METH_VARARGS, "Get ground truth trajectories"},
@@ -1065,6 +1172,7 @@ static PyMethodDef methods[] = {
      "Get road edge polyline counts from vectorized env"},
     {"vec_get_road_edge_polylines", vec_get_road_edge_polylines, METH_VARARGS,
      "Get road edge polylines from vectorized env"},
+    {"vec_stop_recorder", vec_stop_recorder, METH_VARARGS, "Stop the ffmpeg recorder for a specific env"},
     MY_METHODS,
     {NULL, NULL, 0, NULL}};
 
@@ -1084,10 +1192,19 @@ PyMODINIT_FUNC PyInit_binding(void) {
     PyModule_AddIntConstant(m, "MAX_AGENTS", MAX_AGENTS);
     PyModule_AddIntConstant(m, "TRAJECTORY_LENGTH", TRAJECTORY_LENGTH);
     PyModule_AddIntConstant(m, "MAX_ENTITIES_PER_CELL", MAX_ENTITIES_PER_CELL);
+    PyModule_AddIntConstant(m, "NUM_ACCEL_BINS", NUM_ACCEL_BINS);
+    PyModule_AddIntConstant(m, "NUM_STEER_BINS", NUM_STEER_BINS);
+    PyModule_AddIntConstant(m, "NUM_DX_BINS", NUM_DX_BINS);
+    PyModule_AddIntConstant(m, "NUM_DY_BINS", NUM_DY_BINS);
+    PyModule_AddIntConstant(m, "NUM_YAW_BINS", NUM_YAW_BINS);
+    PyModule_AddIntConstant(m, "LAMBDA_CONDITIONING_IDX", LAMBDA_CONDITIONING_IDX);
+    PyModule_AddIntConstant(m, "REWARD_COLLISION_IDX", REWARD_COLLISION_IDX);
+    PyModule_AddIntConstant(m, "REWARD_OFFROAD_COLLISION_IDX", REWARD_OFFROAD_COLLISION_IDX);
+    PyModule_AddIntConstant(m, "REWARD_GOAL_IDX", REWARD_GOAL_IDX);
 
     PyModule_AddIntConstant(m, "ROAD_FEATURES", ROAD_FEATURES);
     PyModule_AddIntConstant(m, "PARTNER_FEATURES", PARTNER_FEATURES);
-    PyModule_AddIntConstant(m, "EGO_FEATURES_CLASSIC", EGO_FEATURES_CLASSIC);
+    PyModule_AddIntConstant(m, "EGO_FEATURES", EGO_FEATURES); // Default
     PyModule_AddIntConstant(m, "EGO_FEATURES_JERK", EGO_FEATURES_JERK);
 
     return m;
diff --git a/pufferlib/ocean/env_config.h b/pufferlib/ocean/env_config.h
index 10f592077f..ed19ff53ea 100644
--- a/pufferlib/ocean/env_config.h
+++ b/pufferlib/ocean/env_config.h
@@ -11,11 +11,12 @@ typedef struct {
     int render_mode;
     int action_type;
     int dynamics_model;
+    bool fix_rewards;
+    bool fix_lambdas;
+    float lambda_value;
     float reward_vehicle_collision;
     float reward_offroad_collision;
     float reward_goal;
-    float reward_goal_post_respawn;
-    float reward_vehicle_collision_post_respawn;
     float goal_radius;
     float goal_speed;
     int collision_behavior;
@@ -31,6 +32,11 @@ typedef struct {
     int control_mode;
     int max_controlled_agents;
     char map_dir[256];
+    char anchor_policy_path[256];
+    int reg_mode;
+    float obs_partner_noise_pos;
+    float obs_partner_noise_speed;
+    bool async_resets;
 } env_init_config;
 
 // INI file parser handler - parses all environment configuration from drive.ini
@@ -52,9 +58,11 @@ static int handler(void *config, const char *section, const char *name, const ch
             env_config->dynamics_model = 0; // CLASSIC
         } else if (strcmp(value, "\"jerk\"") == 0 || strcmp(value, "jerk") == 0) {
             env_config->dynamics_model = 1; // JERK
+        } else if (strcmp(value, "\"delta_local\"") == 0 || strcmp(value, "delta_local") == 0) {
+            env_config->dynamics_model = 2; // DELTA_LOCAL
         } else {
-            printf("Warning: Unknown dynamics_model value '%s', defaulting to JERK\n", value);
-            env_config->dynamics_model = 1; // Default to JERK
+            printf("Warning: Unknown dynamics_model value '%s', defaulting to CLASSIC\n", value);
+            env_config->dynamics_model = 0; // Default to CLASSIC
         }
     } else if (MATCH("env", "goal_behavior")) {
         env_config->goal_behavior = atoi(value);
@@ -62,14 +70,14 @@ static int handler(void *config, const char *section, const char *name, const ch
         env_config->goal_target_distance = atof(value);
     } else if (MATCH("env", "reward_vehicle_collision")) {
         env_config->reward_vehicle_collision = atof(value);
+    } else if (MATCH("env", "obs_partner_noise_speed")) {
+        env_config->obs_partner_noise_speed = atof(value);
+    } else if (MATCH("env", "obs_partner_noise_pos")) {
+        env_config->obs_partner_noise_pos = atof(value);
     } else if (MATCH("env", "reward_offroad_collision")) {
         env_config->reward_offroad_collision = atof(value);
     } else if (MATCH("env", "reward_goal")) {
         env_config->reward_goal = atof(value);
-    } else if (MATCH("env", "reward_goal_post_respawn")) {
-        env_config->reward_goal_post_respawn = atof(value);
-    } else if (MATCH("env", "reward_vehicle_collision_post_respawn")) {
-        env_config->reward_vehicle_collision_post_respawn = atof(value);
     } else if (MATCH("env", "goal_radius")) {
         env_config->goal_radius = atof(value);
     } else if (MATCH("env", "goal_speed")) {
@@ -112,6 +120,18 @@ static int handler(void *config, const char *section, const char *name, const ch
             printf("Warning: Unknown control_mode value '%s', defaulting to CONTROL_VEHICLES\n", value);
             env_config->control_mode = 0; // Default to CONTROL_VEHICLES
         }
+    } else if (MATCH("env", "reg_mode")) {
+        if (strcmp(value, "\"None\"") == 0 || strcmp(value, "None") == 0 || strcmp(value, "\"none\"") == 0 ||
+            strcmp(value, "none") == 0) {
+            env_config->reg_mode = 0; // NONE
+        } else if (strcmp(value, "\"log_prob_direct\"") == 0 || strcmp(value, "log_prob_direct") == 0) {
+            env_config->reg_mode = 1; // LOG_PROB_DIRECT
+        } else if (strcmp(value, "\"kl_anchor\"") == 0 || strcmp(value, "kl_anchor") == 0) {
+            env_config->reg_mode = 2; // KL_ANCHOR
+        } else {
+            printf("Warning: Unknown reg_mode value '%s', defaulting to NONE\n", value);
+            env_config->reg_mode = 0; // Default to NONE
+        }
     } else if (MATCH("env", "map_dir")) {
         if (sscanf(value, "\"%255[^\"]\"", env_config->map_dir) != 1) {
             strncpy(env_config->map_dir, value, sizeof(env_config->map_dir) - 1);
@@ -120,6 +140,20 @@ static int handler(void *config, const char *section, const char *name, const ch
         // printf("Parsed map_dir: '%s'\n", env_config->map_dir);
     } else if (MATCH("env", "max_controlled_agents")) {
         env_config->max_controlled_agents = atoi(value);
+    } else if (MATCH("env", "anchor_policy_path")) {
+        if (sscanf(value, "\"%255[^\"]\"", env_config->anchor_policy_path) != 1) {
+            strncpy(env_config->anchor_policy_path, value, sizeof(env_config->anchor_policy_path) - 1);
+            env_config->anchor_policy_path[sizeof(env_config->anchor_policy_path) - 1] = '\0';
+        }
+    } else if (MATCH("env", "async_resets")) {
+        env_config->async_resets =
+            (strcmp(value, "True") == 0 || strcmp(value, "true") == 0 || strcmp(value, "1") == 0);
+    } else if (MATCH("env", "fix_rewards")) {
+        env_config->fix_rewards = (strcmp(value, "True") == 0 || strcmp(value, "true") == 0 || strcmp(value, "1") == 0);
+    } else if (MATCH("env", "fix_lambdas")) {
+        env_config->fix_lambdas = (strcmp(value, "True") == 0 || strcmp(value, "true") == 0 || strcmp(value, "1") == 0);
+    } else if (MATCH("env", "lambda_value")) {
+        env_config->lambda_value = atof(value);
     } else {
         return 0; // Unknown section/name, indicate failure to handle
     }
diff --git a/pufferlib/pufferl.py b/pufferlib/pufferl.py
index 2712ab02d1..909e9551ba 100644
--- a/pufferlib/pufferl.py
+++ b/pufferlib/pufferl.py
@@ -129,6 +129,10 @@ def __init__(self, config, vecenv, policy, logger=None, full_args=None):
         self.ep_lengths = torch.zeros(total_agents, device=device, dtype=torch.int32)
         self.ep_indices = torch.arange(total_agents, device=device, dtype=torch.int32)
         self.free_idx = total_agents
+        # Post-terminal masking for multi-agent envs
+        # Tracks which agents have hit a terminal and should be ignored until world reset
+        self.agent_dead = torch.zeros(total_agents, device=device, dtype=torch.bool)
+        self.masks = torch.ones(segments, rollout_horizon, device=device)
 
         # LSTM
         if config["use_rnn"]:
@@ -224,8 +228,21 @@ def __init__(self, config, vecenv, policy, logger=None, full_args=None):
         self.utilization = Utilization()
         self.profile = Profile()
         self.stats = defaultdict(list)
+        self.eval_stats = {}
         self.last_stats = defaultdict(list)
         self.losses = {}
+        self.data = {}
+
+        # We only do this for the driver env to save memory
+        self.reg_mode = full_args["env"]["reg_mode"]
+        self.bc_anchor, self.data = self.vecenv.driver_env._init_regularization_strategy(device=config["device"])
+        self.resample_freq_epoch = self.vecenv.driver_env.resample_frequency / self.vecenv.driver_env.episode_length
+
+        # Lambda conditioning
+        self.lambda_obs_idx = self.vecenv.driver_env.lambda_obs_idx
+        self.reward_veh_obs_idx = self.vecenv.driver_env.reward_veh_obs_idx
+        self.reward_offroad_obs_idx = self.vecenv.driver_env.reward_offroad_obs_idx
+        self.reward_goal_obs_idx = self.vecenv.driver_env.reward_goal_obs_idx
 
         # Dashboard
         self.model_size = sum(p.numel() for p in policy.parameters() if p.requires_grad)
@@ -308,6 +325,21 @@ def evaluate(self):
 
                 self.actions[batch_rows, l] = action
                 self.logprobs[batch_rows, l] = logprob
+
+                # The terminal timestep itself is valid; everything after is not.
+                # We store the mask before updating agent_dead so the terminal step is included.
+                terminal_flag = (d > 0) & (t == 0)  # true terminal (not truncation)
+                truncation_flag = t > 0  # world-level reset
+
+                valid = (~self.agent_dead[env_id]).float()
+                self.masks[batch_rows, l] = valid
+
+                # Mark agents dead for all future timesteps in this segment
+                self.agent_dead[env_id] = self.agent_dead[env_id] | terminal_flag.bool()
+
+                # World reset clears dead status
+                self.agent_dead[env_id] = self.agent_dead[env_id] & ~truncation_flag.bool()
+
                 # Truncation bootstrap hack for auto-reset envs.
                 # Ideally we add `gamma * V(s_{t+1})` on truncation steps, but Drive resets in C so
                 # the value at index `l` is post-reset. We use `values[..., l-1]` as a heuristic
@@ -350,6 +382,31 @@ def evaluate(self):
         self.free_idx = self.total_agents
         self.ep_indices = torch.arange(self.total_agents, device=device, dtype=torch.int32)
         self.ep_lengths.zero_()
+
+        if self.reg_mode == "log_prob_direct" and (self.epoch + 1) % self.resample_freq_epoch == 0:
+            # Occasionally resample human demonstrations in the driver environment.
+            # This follows the same logic used for map resampling.
+            _, self.data = self.vecenv.driver_env._init_regularization_strategy(
+                reg_mode=self.reg_mode,
+                device=config["device"],
+            )
+
+        # --- Apply post-terminal mask to buffers before training ---
+        # Zero rewards and values for invalid (post-terminal) timesteps
+        self.rewards *= self.masks
+        self.values *= self.masks
+        # Force dones=1 for invalid timesteps so advantage propagation is cut
+        self.terminals[self.masks == 0] = 1.0
+
+        self.perc_transitions_trained_on = self.masks.mean().item()
+
+        # Reset agent_dead for next rollout
+        self.agent_dead.zero_()
+
+        # # TEMP
+        # self.masks.fill_(1.0)
+        # self.perc_transitions_trained_on = 1.0
+
         profile.end()
         return self.stats
 
@@ -403,6 +460,7 @@ def train(self):
             mb_values = self.values[idx]
             mb_returns = advantages[idx] + mb_values
             mb_advantages = advantages[idx]
+            mb_masks = self.masks[idx]
 
             profile("train_forward", epoch)
             if not config["use_rnn"]:
@@ -428,6 +486,94 @@ def train(self):
                 approx_kl = ((ratio - 1) - logratio).mean()
                 clipfrac = ((ratio - 1.0).abs() > config["clip_coef"]).float().mean()
 
+            if self.reg_mode == "kl_anchor":
+                # Flatten the batch and time dimensions for feeding into the BC anchor policy since it is trained without memory
+                # [B, rollout_horizon, obs_dim] -> [B * rollout_horizon, obs_dim]
+                anchor_obs = mb_obs.reshape(-1, *self.vecenv.single_observation_space.shape)
+                # Set all conditioning weights to zero because that is what we train the anchor with.
+                # lambda conditioning only has a meaning for the self-play RL training and policy.
+                bc_anchor_obs = anchor_obs.clone()
+
+                bc_anchor_obs[:, self.lambda_obs_idx] = 0.0
+                bc_anchor_obs[:, self.reward_veh_obs_idx] = -1.0
+                bc_anchor_obs[:, self.reward_offroad_obs_idx] = -1.0
+                bc_anchor_obs[:, self.reward_goal_obs_idx] = 1.0
+
+                with torch.no_grad():
+                    # Returns a list of tensors, one per action head
+                    # Classic: [logits_joint]. Delta_local: [logits_dx, logits_dy, logits_yaw].
+                    anchor_logits_list = self.bc_anchor.get_action_dist_logits(bc_anchor_obs)
+
+                # Compute per-sample KL summed across all independent action heads
+                num_heads = len(anchor_logits_list)
+                per_sample_kl = torch.zeros(anchor_obs.shape[0], device=device)
+                anchor_entropy_accum = 0.0
+
+                for h in range(num_heads):
+                    with torch.no_grad():
+                        anchor_lp = torch.log_softmax(anchor_logits_list[h], dim=-1)
+                        anchor_entropy_accum += pufferlib.pytorch.entropy(anchor_lp).mean().item()
+
+                    cur_lp = torch.log_softmax(logits[h].reshape(-1, logits[h].shape[-1]), dim=-1)
+                    per_sample_kl = per_sample_kl + torch.nn.functional.kl_div(
+                        cur_lp, anchor_lp, reduction="none", log_target=True
+                    ).sum(dim=-1)
+
+                self.sampled_lambdas = anchor_obs[:, self.lambda_obs_idx].unsqueeze(-1)  # [B, 1]
+                self.sampled_collision_rewards = anchor_obs[:, self.reward_veh_obs_idx].unsqueeze(-1)  # [B, 1]
+
+                unweighted_reg_loss = per_sample_kl.mean()
+
+                # Weight by lambda to get batch average
+                reg_loss = (per_sample_kl * self.sampled_lambdas.squeeze(-1)).mean()
+
+                with torch.no_grad():
+                    self.data["data/anchor_entropy"] = anchor_entropy_accum / num_heads
+
+            elif self.reg_mode == "log_prob_direct":
+                discrete_human_actions, continuous_human_actions, human_observations = (
+                    self.vecenv.driver_env.sample_human_demonstrations()
+                )
+                use_continuous = self.vecenv.driver_env._action_type_flag == 1
+                human_actions = continuous_human_actions if use_continuous else discrete_human_actions
+                human_actions = human_actions.reshape(-1, 1).to(device)
+                human_observations = human_observations.to(device)
+
+                # 2: Compute the log-likelihood of human actions under the current policy,
+                # given the corresponding human observations. Higher (less negative) log-likelihood
+                # indicates that the policy behaves more like a human under the same observations.
+                human_state = dict(
+                    lstm_h=None,
+                    lstm_c=None,
+                )
+
+                human_logits, _ = self.policy(human_observations, human_state)
+                _, human_log_prob, human_entropy = pufferlib.pytorch.sample_logits(
+                    logits=human_logits, action=human_actions
+                )
+
+                with torch.no_grad():
+                    # Policy actions and corresponding entropy at human observations
+                    policy_actions, _, policy_entropy = pufferlib.pytorch.sample_logits(human_logits)
+
+                    # Estimate how off-policy the human observations are relative to the on-policy samples.
+                    # The assumption is that if the human observations are very off-policy,
+                    # then the entropy of sampled actions conditioned on the human observation is higher than
+                    # the entropy at the on-policy collected observations, since the policy is more uncertain about which action to take.
+                    _, human_log_prob_obs, human_entropy_obs = pufferlib.pytorch.sample_logits(logits=human_logits)
+
+                    relative_entropy = human_entropy_obs.mean() - entropy.mean()
+                    logratio_human_data = human_log_prob_obs - mb_logprobs[: human_log_prob_obs.shape[0], 0]
+                    human_obs_importance = logratio_human_data.exp()
+
+                # Average and clip
+                human_log_prob = torch.clamp(human_log_prob, -15, 0)
+                reg_loss = -human_log_prob.mean()
+
+            else:
+                unweighted_reg_loss = torch.tensor(0.0, device=device)
+                reg_loss = torch.tensor(0.0, device=device)
+
             adv = advantages[idx]
             adv = compute_puff_advantage(
                 mb_values,
@@ -441,9 +587,20 @@ def train(self):
                 config["vtrace_c_clip"],
             )
             adv = mb_advantages
-            adv = mb_prio * (adv - adv.mean()) / (adv.std() + 1e-8)
+            adv = (adv - adv.mean()) / (adv.std() + 1e-8)
+
+            # --- Masked advantage normalization ---
+            # Only compute mean/std over valid timesteps
+            valid_adv = adv[mb_masks == 1]
+            if valid_adv.numel() > 0:
+                adv_mean = valid_adv.mean()
+                adv_std = valid_adv.std() + 1e-8
+            else:
+                adv_mean = adv.mean()
+                adv_std = adv.std() + 1e-8
+            adv = (adv - adv_mean) / adv_std
+            adv = adv * mb_masks  # zero out invalid timesteps
 
-            # Losses
             pg_loss1 = -adv * ratio
             pg_loss2 = -adv * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef)
             pg_loss = torch.max(pg_loss1, pg_loss2).mean()
@@ -462,9 +619,8 @@ def train(self):
                 current_ent_coef = 0.5 * self.ent_coef_initial * (1 + np.cos(np.pi * self.epoch / self.total_epochs))
             else:
                 current_ent_coef = config["ent_coef"]
-
-            loss = pg_loss + config["vf_coef"] * v_loss - current_ent_coef * entropy_loss
-
+            loss = pg_loss + config["vf_coef"] * v_loss - current_ent_coef * entropy_loss + reg_loss
+            # loss = reg_loss
             self.amp_context.__enter__()  # TODO: AMP needs some debugging
 
             # This breaks vloss clipping?
@@ -479,6 +635,7 @@ def train(self):
             losses["approx_kl"] += approx_kl.item() / self.total_minibatches
             losses["clipfrac"] += clipfrac.item() / self.total_minibatches
             losses["importance"] += ratio.mean().item() / self.total_minibatches
+            losses["reg_loss"] += unweighted_reg_loss / self.total_minibatches
 
             # Learn on accumulated minibatches
             profile("learn", epoch)
@@ -503,41 +660,70 @@ def train(self):
         logs = None
         self.epoch += 1
         done_training = self.global_step >= config["total_timesteps"]
-        if done_training or self.global_step == 0 or time.time() > self.last_log_time + 0.25:
-            logs = self.mean_and_log()
-            self.losses = losses
-            self.print_dashboard()
-            self.stats = defaultdict(list)
-            self.last_log_time = time.time()
-            self.last_log_step = self.global_step
-            profile.clear()
 
-        if self.epoch % config["checkpoint_interval"] == 0 or done_training:
-            self.save_checkpoint()
-            self.msg = f"Checkpoint saved at update {self.epoch}"
-
-        if (self.epoch - 1) % self.config["eval"]["eval_interval"] == 0 or done_training:
+        # Intermittent evaluations
+        if self.epoch % self.config["eval"]["eval_interval"] == 0 or done_training:
             human_replay_eval = self.config["eval"]["human_replay_eval"]
             self_play_eval = self.config["eval"]["self_play_eval"]
 
             self.evaluator = Evaluator(self.full_args, self.logger)
+
             if human_replay_eval:
                 self.evaluator.hr_env = load_env("puffer_drive", self.evaluator.hr_eval_config)
-                self.evaluator.rollout(self.uncompiled_policy, mode="human_replay")
+                try:
+                    self.evaluator.rollout(self.uncompiled_policy, mode="human_replay")
+                except Exception as e:
+                    print(f"Render failed (non-fatal): {e}")
+                self.evaluator.hr_env.driver_env.stop_recorder(0)
                 self.evaluator.hr_env.close()
                 self.evaluator.log_videos(eval_mode="human_replay", epoch=self.epoch)
+
             if self_play_eval:
                 self.evaluator.sp_env = load_env("puffer_drive", self.evaluator.sp_eval_config)
-                self.evaluator.rollout(self.uncompiled_policy, mode="self_play")
+                try:
+                    self.evaluator.rollout(self.uncompiled_policy, mode="self_play")
+                except Exception as e:
+                    print(f"Render failed (non-fatal): {e}")
+                self.evaluator.sp_env.driver_env.stop_recorder(0)
                 self.evaluator.sp_env.close()
                 self.evaluator.log_videos(eval_mode="self_play", epoch=self.epoch)
+
+            # Show agent view with sensor noise
+            # sensor_noise_eval = self.epoch % 1000 == 0
+            # if sensor_noise_eval:
+            #     self.evaluator.hr_env = load_env("puffer_drive", self.evaluator.hr_eval_config)
+            #     try:
+            #         self.evaluator.rollout(
+            #             self.uncompiled_policy,
+            #             mode="human_replay",
+            #             view_mode=3,
+            #         )
+            #     except Exception as e:
+            #         print(f"Sensor noise render failed (non-fatal): {e}")
+            #     self.evaluator.hr_env.close()
+            #     self.evaluator.log_videos(eval_mode="sensor_noise", epoch=self.epoch)
+
+            # Get statistics
             if human_replay_eval or self_play_eval:
-                self.evaluator.log_stats()
+                eval_statistics = self.evaluator.collect_stats()
+                self.eval_stats.update(eval_statistics)
+                del self.evaluator
+
+        if done_training or self.global_step == 0 or time.time() > self.last_log_time + 0.25:
+            logs = self.mean_and_log()
+            self.losses = losses
+            self.print_dashboard()
+            self.stats = defaultdict(list)
+            self.last_log_time = time.time()
+            self.last_log_step = self.global_step
+            profile.clear()
 
-            del self.evaluator
+        if self.epoch % config["checkpoint_interval"] == 0 or done_training:
+            self.save_checkpoint()
+            self.msg = f"Checkpoint saved at update {self.epoch}"
 
-        if self.config["eval"]["wosac_realism_eval"]:
-            pufferlib.utils.run_wosac_eval_in_subprocess(self.config, self.logger, self.global_step)
+            if self.config["eval"]["wosac_realism_eval"]:
+                pufferlib.utils.run_wosac_eval_in_subprocess(self.config, self.logger, self.global_step)
 
     def mean_and_log(self):
         config = self.config
@@ -551,23 +737,48 @@ def mean_and_log(self):
             self.stats[k] = v
 
         device = config["device"]
+
         agent_steps = int(dist_sum(self.global_step, device))
+        ent_coef = (
+            0.5 * self.ent_coef_initial * (1 + np.cos(np.pi * self.epoch / self.total_epochs))
+            if config["anneal_entropy"]
+            else config["ent_coef"]
+        )
+
         logs = {
             "SPS": dist_sum(self.sps, device),
             "agent_steps": agent_steps,
+            "environment/perc_transitions_used": self.perc_transitions_trained_on,
             "uptime": time.time() - self.start_time,
             "epoch": int(dist_sum(self.epoch, device)),
             "learning_rate": self.optimizer.param_groups[0]["lr"],
-            "ent_coef": (
-                0.5 * self.ent_coef_initial * (1 + np.cos(np.pi * self.epoch / self.total_epochs))
-                if config["anneal_entropy"]
-                else config["ent_coef"]
-            ),
+            "ent_coef": ent_coef,
             **{f"environment/{k}": v for k, v in self.stats.items()},
             **{f"losses/{k}": v for k, v in self.losses.items()},
             **{f"performance/{k}": v["elapsed"] for k, v in self.profile},
+            "data/anchor_entropy": self.data.get("data/anchor_entropy", 0),
         }
 
+        if self.eval_stats:
+            logs.update(self.eval_stats)
+
+        if hasattr(self, "sampled_lambdas"):
+            logs["data/lambda_mean"] = float(self.sampled_lambdas.mean())
+            logs["data/lambda_std"] = float(self.sampled_lambdas.std())
+
+        if isinstance(self.logger, WandbLogger):
+            import wandb
+
+            if hasattr(self, "sampled_lambdas"):
+                logs["data/lambda_distrib"] = wandb.Histogram(self.sampled_lambdas.cpu().numpy())
+            if hasattr(self, "sampled_collision_rewards"):
+                logs["data/collision_reward_distrib"] = wandb.Histogram(self.sampled_collision_rewards.cpu().numpy())
+
+        if self.reg_mode == "log_prob_direct":
+            logs["data/total_human_samples"] = self.data.get("data/total_human_samples", 0)
+            logs["data/unique_human_samples"] = self.data.get("data/unique_human_samples", 0)
+            logs["data/perc_unique_human_samples"] = self.data.get("data/perc_unique_human_samples", 0)
+
         if torch.distributed.is_initialized():
             if torch.distributed.get_rank() != 0:
                 self.logger.log(logs, agent_steps)
@@ -615,8 +826,48 @@ def save_checkpoint(self):
         state_path = os.path.join(path, "trainer_state.pt")
         torch.save(state, state_path + ".tmp")
         os.rename(state_path + ".tmp", state_path)
+
+        # Upload checkpoint to wandb
+        if isinstance(self.logger, WandbLogger):
+            import wandb
+
+            artifact = wandb.Artifact(
+                f"checkpoint-{run_id}-epoch{self.epoch:06d}",
+                type="checkpoint",
+                metadata={"epoch": self.epoch, "global_step": self.global_step},
+            )
+            artifact.add_file(model_path)
+            artifact.add_file(state_path)
+            self.logger.wandb.run.log_artifact(artifact)
+
         return model_path
 
+    def _export_to_bin(self):
+        """Export latest checkpoint to .bin for rendering."""
+        model_dir = os.path.join(self.config["data_dir"], f"{self.config['env']}_{self.logger.run_id}")
+        model_files = glob.glob(os.path.join(model_dir, "model_*.pt"))
+
+        if not model_files:
+            return None
+
+        latest_cpt = max(model_files, key=os.path.getctime)
+        bin_path = f"{model_dir}.bin"
+
+        try:
+            export_args = {"env_name": self.config["env"], "load_model_path": latest_cpt, **self.config}
+            export(
+                args=export_args,
+                env_name=self.config["env"],
+                vecenv=self.vecenv,
+                policy=self.uncompiled_policy,
+                path=bin_path,
+                silent=True,
+            )
+            return bin_path
+        except Exception as e:
+            print(f"Failed to export model weights: {e}")
+            return None
+
     def print_dashboard(self, clear=False, idx=[0], c1="[cyan]", c2="[white]", b1="[bright_cyan]", b2="[bright_white]"):
         config = self.config
         sps = dist_sum(self.sps, config["device"])
@@ -810,8 +1061,8 @@ def __call__(self, name, epoch, nest=False):
         if epoch % self.frequency != 0:
             return
 
-        # if torch.cuda.is_available():
-        #    torch.cuda.synchronize()
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
 
         tick = time.time()
         if len(self.stack) != 0 and not nest:
@@ -827,8 +1078,8 @@ def pop(self, end):
         profile["delta"] += delta
 
     def end(self):
-        # if torch.cuda.is_available():
-        #    torch.cuda.synchronize()
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
 
         end = time.time()
         for i in range(len(self.stack)):
@@ -943,15 +1194,16 @@ class WandbLogger:
     def __init__(self, args, load_id=None, resume="allow"):
         import wandb
 
+        run_name = args.get("wandb_run_name", None)
         wandb.init(
             id=load_id or wandb.util.generate_id(),
+            name=run_name,
             project=args["wandb_project"],
             group=args["wandb_group"],
             allow_val_change=True,
             save_code=False,
             resume=resume,
             config=args,
-            name=args.get("wandb_name"),
             tags=[args["tag"]] if args["tag"] is not None else [],
         )
         self.wandb = wandb
@@ -987,6 +1239,13 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None):
         torch.cuda.set_device(local_rank)
         os.environ["CUDA_VISIBLE_DEVICES"] = str(local_rank)
 
+    if args["rnn_name"] is None:
+        args["env"]["uses_memory"] = False
+        args["env"]["memory_size"] = 0
+    else:
+        args["env"]["uses_memory"] = True
+        args["env"]["memory_size"] = args["train"]["rollout_horizon"]
+
     vecenv = vecenv or load_env(env_name, args)
     policy = policy or load_policy(args, vecenv, env_name)
 
@@ -1021,7 +1280,7 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None):
 
         if logs is not None:
             if pufferl.global_step > 0.20 * train_config["total_timesteps"]:
-                all_logs.append(logs)
+                all_logs = [logs]
 
     # Final eval. You can reset the env here, but depending on
     # your env, this can skew data (i.e. you only collect the shortest
@@ -1049,7 +1308,6 @@ def eval(env_name, args=None, vecenv=None, policy=None):
     args["env"]["termination_mode"] = 0
 
     wosac_enabled = args["eval"]["wosac_realism_eval"]
-    human_replay_enabled = args["eval"]["human_replay_eval"]
 
     if wosac_enabled:
         args["env"]["map_dir"] = args["eval"]["map_dir"]
@@ -1128,27 +1386,114 @@ def eval(env_name, args=None, vecenv=None, policy=None):
             frame_count = 0
 
         while True:
-            driver.render()
+            driver.render(env_id=0)
 
             with torch.no_grad():
                 ob = torch.as_tensor(ob).to(device)
                 logits, value = policy.forward_eval(ob, state)
+
                 action, logprob, _ = pufferlib.pytorch.sample_logits(logits)
+
                 action = action.cpu().numpy().reshape(vecenv.action_space.shape)
 
             if isinstance(logits, torch.distributions.Normal):
                 action = np.clip(action, vecenv.action_space.low, vecenv.action_space.high)
 
-            ob, reward, done, truncated, info = vecenv.step(action)
+            obs, reward, terminal, truncated, info = vecenv.step(action)
 
             if driver.render_mode == 1:
                 frame_count += 1
-                if frame_count >= max_frames or done.all() or truncated.all():
+                if frame_count >= max_frames or terminal.all() or truncated.all():
                     break
 
         vecenv.close()
 
 
+def verify(env_name, args=None, vecenv=None):
+    """Verify human demonstrations or BC policy behavior."""
+
+    args = args or load_config(env_name)
+    args["env"]["termination_mode"] = 0
+
+    # Determine verification mode: "bc_policy", "expert_replay", or "inferred_expert_actions"
+    verify_mode = "inferred_expert_actions"  # args.get("verify_mode", "inferred_expert_actions")
+
+    if verify_mode == "bc_policy":
+        # BC policy controls all agents directly
+        args["env"]["control_mode"] = "control_agents"
+        args["env"]["reg_mode"] = "kl_anchor"  # Needed to trigger BC loading
+        args["env"]["control_mode"] = "control_agents"
+        args["env"]["fix_lambdas"] = True
+        args["env"]["fix_rewards"] = True
+        args["env"]["lambda_value"] = 0.0
+        args["env"]["reward_vehicle_collision"] = -1.0
+        args["env"]["reward_offroad_collision"] = -1.0
+        args["env"]["reward_goal"] = 1.0
+    else:
+        # Options: "expert_replay", "inferred_expert_actions"
+        args["env"]["control_mode"] = verify_mode
+
+    backend = args["vec"]["backend"]
+    if backend != "PufferEnv":
+        backend = "Serial"
+
+    args["vec"] = dict(backend=backend, num_envs=1)
+
+    vecenv = vecenv or load_env(env_name, args)
+
+    ob, info = vecenv.reset()
+    driver = vecenv.driver_env
+
+    if verify_mode == "bc_policy":
+        # Load BC policy via the existing regularization strategy
+        device = args["train"]["device"]
+        bc_policy, _ = driver._init_regularization_strategy(device=device)
+        if bc_policy is None:
+            raise RuntimeError(
+                "Failed to load BC policy. Ensure anchor_cpt_path is set and a trained BC checkpoint exists."
+            )
+        bc_policy.eval()
+    else:
+        # Populate inferred trajectory arrays for visualization
+        driver._prepare_human_data()
+
+    num_agents = vecenv.observation_space.shape[0]
+    device = args["train"]["device"]
+
+    if driver.render_mode == 1:
+        max_frames = 91
+        frame_count = 0
+
+    while True:
+        driver.render(draw_traces=True)
+
+        if verify_mode == "bc_policy":
+            with torch.no_grad():
+                ob_tensor = torch.as_tensor(ob).to(device)
+                logits_list = bc_policy.get_action_dist_logits(ob_tensor)
+
+                if len(logits_list) == 1:
+                    # Joint action space (classic dynamics)
+                    action = torch.argmax(logits_list[0], dim=-1, keepdim=True)
+                else:
+                    # Multi-head (delta_local)
+                    action = torch.stack([torch.argmax(l, dim=-1) for l in logits_list], dim=-1)
+
+                action = action.cpu().numpy().reshape(vecenv.action_space.shape)
+        else:
+            # Expert replay / inferred expert actions: env handles actions internally, just need a shape
+            action = vecenv.action_space.sample()
+
+        ob, reward, done, truncated, info = vecenv.step(action)
+
+        if driver.render_mode == 1:
+            frame_count += 1
+            if frame_count >= max_frames or done.all() or truncated.all():
+                break
+
+    vecenv.close()
+
+
 def sweep(args=None, env_name=None):
     args = args or load_config(env_name)
     if not args["wandb"] and not args["neptune"]:
@@ -1162,19 +1507,25 @@ def sweep(args=None, env_name=None):
 
     sweep = sweep_cls(args["sweep"])
     points_per_run = args["sweep"]["downsample"]
-    target_key = f"environment/{args['sweep']['metric']}"
+    target_key = f"{args['sweep']['metric']}"
+    last_n = 5  # Points to use
+
     for i in range(args["max_runs"]):
-        seed = time.time_ns() & 0xFFFFFFFF
+        seed = 42
         random.seed(seed)
         np.random.seed(seed)
         torch.manual_seed(seed)
         sweep.suggest(args)
+
         total_timesteps = args["train"]["total_timesteps"]
         all_logs = train(env_name, args=args)
         all_logs = [e for e in all_logs if target_key in e]
-        scores = downsample([log[target_key] for log in all_logs], points_per_run)
+        scores = downsample([log[target_key] for log in all_logs][-last_n:], points_per_run)
         costs = downsample([log["uptime"] for log in all_logs], points_per_run)
         timesteps = downsample([log["agent_steps"] for log in all_logs], points_per_run)
+
+        if len(scores) > 0:
+            print(f"Run {i + 1} | {target_key} avg: {np.mean(scores):.4f}")
         for score, cost, timestep in zip(scores, costs, timesteps):
             args["train"]["total_timesteps"] = timestep
             sweep.observe(args, score, cost)
@@ -1192,39 +1543,74 @@ def controlled_exp(env_name, args=None):
     if not args["wandb"] and not args["neptune"]:
         raise pufferlib.APIUsageError("Targeted experiments require either wandb or neptune")
 
-    # Check if controlled_exp config exists
     if "controlled_exp" not in args:
         raise pufferlib.APIUsageError("No [controlled_exp.*] sections found in config")
 
+    # Map num_maps values to corresponding map directories
+    map_dir_lookup = {
+        100: "resources/drive/binaries/training_100",
+        1000: "resources/drive/binaries/training_1k",
+        10000: "resources/drive/binaries/training_10k",
+        20000: "resources/drive/binaries/training_20k",
+        50000: "resources/drive/binaries/training_50k",
+    }
+
     # Extract parameters from controlled_exp namespace
     params = {}
     for section, section_config in args["controlled_exp"].items():
         if isinstance(section_config, dict):
             for param, param_config in section_config.items():
                 if isinstance(param_config, dict) and "values" in param_config:
-                    params[f"{section}.{param}"] = param_config["values"]
+                    processed_values = []
+                    for val in param_config["values"]:
+                        if isinstance(val, str) and val == "None":
+                            processed_values.append(None)
+                        else:
+                            processed_values.append(val)
+                    params[f"{section}.{param}"] = processed_values
 
     if not params:
         raise pufferlib.APIUsageError("No parameters with 'values' lists found in [controlled_exp.*] sections")
 
-    # Generate all combinations
     keys = list(params.keys())
     combinations = list(itertools.product(*[params[k] for k in keys]))
 
     print(f"Running a total of {len(combinations)} experiments with parameters: {keys}")
 
-    # Run each combination
     for i, combo in enumerate(combinations, 1):
         exp_args = deepcopy(args)
 
-        # Set parameters
         for key, value in zip(keys, combo):
             section, param = key.split(".")
             exp_args[section][param] = value
 
+        # Auto-set map_dir based on num_maps if present in sweep
+        if "env.num_maps" in keys:
+            num_maps = exp_args["env"]["num_maps"]
+            if num_maps in map_dir_lookup:
+                exp_args["env"]["map_dir"] = map_dir_lookup[num_maps]
+
+        # Create descriptive name (exclude map_dir)
+        run_name_parts = []
+        for key, value in zip(keys, combo):
+            param_name = key.split(".")[-1]
+            if param_name == "map_dir":
+                continue
+            if value is None:
+                value_str = "None"
+            elif isinstance(value, bool):
+                value_str = str(value)
+            elif isinstance(value, (int, float)):
+                value_str = str(value)
+            else:
+                value_str = str(value)
+            run_name_parts.append(f"{param_name}={value_str}")
+
+        exp_name = "_".join(run_name_parts)
+        exp_args["wandb_run_name"] = f"{exp_name}"
+
         print(f"\nExperiment {i}/{len(combinations)}: {dict(zip(keys, combo))}")
 
-        # Train
         train(env_name, args=exp_args)
 
     print(f"\n✓ Completed all {len(combinations)} experiments")
@@ -1489,6 +1875,8 @@ def main():
         train(env_name=env_name)
     elif mode == "eval":
         eval(env_name=env_name)
+    elif mode == "verify":
+        verify(env_name=env_name)
     elif mode == "sweep":
         sweep(env_name=env_name)
     elif mode == "controlled_exp":
diff --git a/pufferlib/utils.py b/pufferlib/utils.py
index e66a37165d..600acb51d5 100644
--- a/pufferlib/utils.py
+++ b/pufferlib/utils.py
@@ -49,7 +49,7 @@ def run_wosac_eval_in_subprocess(config, logger, global_step):
             "--eval.wosac-goal-behavior",
             str(eval_config.get("wosac_goal_behavior", 2)),
             "--eval.wosac-goal-radius",
-            str(eval_config.get("wosac_goal_radius", 2.0)),
+            str(eval_config.get("wosac_goal_radius", 1.0)),
             "--eval.wosac-sanity-check",
             str(eval_config.get("wosac_sanity_check", False)),
         ]
@@ -76,14 +76,19 @@ def run_wosac_eval_in_subprocess(config, logger, global_step):
                 if hasattr(logger, "wandb") and logger.wandb:
                     logger.wandb.log(
                         {
-                            "eval/wosac_realism_meta_score": wosac_metrics["realism_meta_score"],
-                            "eval/realism_meta_score_std": wosac_metrics["realism_meta_score_std"],
+                            "eval/wosac_realism_meta_score_mean": wosac_metrics["realism_meta_score"],
+                            "eval/wosac_realism_meta_score_std": wosac_metrics["realism_meta_score_std"],
                             "eval/wosac_kinematic_metrics": wosac_metrics["kinematic_metrics"],
                             "eval/wosac_interactive_metrics": wosac_metrics["interactive_metrics"],
                             "eval/wosac_map_based_metrics": wosac_metrics["map_based_metrics"],
                             "eval/wosac_ade": wosac_metrics["ade"],
                             "eval/wosac_min_ade": wosac_metrics["min_ade"],
-                            "eval/wosac_total_num_agents": wosac_metrics["total_num_agents"],
+                            # "eval/wosac_likelihood_ttc": wosac_metrics["likelihood_time_to_collision"],
+                            # "eval/wosac_likelihood_collision": wosac_metrics["likelihood_collision_indication"],
+                            # "eval/wosac_likelihood_dist_to_no": wosac_metrics["likelihood_distance_to_nearest_object"],
+                            # "eval/num_collisions_sim": wosac_metrics["num_collisions_sim"],
+                            # "eval/num_collisions_ref": wosac_metrics["num_collisions_ref"],
+                            # "eval/wosac_total_num_agents": wosac_metrics["total_num_agents"],
                         },
                         step=global_step,
                     )
diff --git a/scripts/evaluate_drive_policy_dataset.py b/scripts/evaluate_drive_policy_dataset.py
new file mode 100644
index 0000000000..c15abf8ce5
--- /dev/null
+++ b/scripts/evaluate_drive_policy_dataset.py
@@ -0,0 +1,745 @@
+#!/usr/bin/env python3
+"""Run a trained PufferDrive policy once per controllable agent across a map dataset."""
+
+from __future__ import annotations
+
+import argparse
+import ast
+import contextlib
+import json
+import os
+import random
+import re
+import shutil
+import struct
+import sys
+import tempfile
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+import pandas as pd
+import torch
+from tqdm import tqdm
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+import pufferlib
+from pufferlib.ocean.drive import binding
+from pufferlib.ocean.drive.drive import Drive, RenderView
+from pufferlib.pufferl import load_config, load_policy
+
+
+RENDER_VIEW_MAP = {
+    "full_sim_state": RenderView.FULL_SIM_STATE,
+    "bev_agent_obs": RenderView.BEV_AGENT_OBS,
+    "agent_persp": RenderView.AGENT_PERSP,
+}
+OBJECT_TYPES = {1, 2, 3}
+SDC_TRACK_INDEX_OFFSET = 16
+LOG_METRIC_KEYS = (
+    "n",
+    "score",
+    "offroad_rate",
+    "collision_rate",
+    "collision_rate_valid",
+    "episode_length",
+    "episode_return",
+    "dnf_rate",
+    "completion_rate",
+    "lane_alignment_rate",
+    "perc_controlled",
+    "perc_other",
+    "offroad_per_agent",
+    "collisions_per_agent",
+    "goals_sampled_this_episode",
+    "goals_reached_this_episode",
+    "speed_at_goal",
+)
+DEFAULT_EVAL_MODES = ("human_replay", "self_play")
+
+
+@dataclass(frozen=True)
+class MapRecord:
+    map_id: int
+    map_name: str
+    source_path: Path
+
+
+def main():
+    args = parse_args()
+    args.policy_path = args.policy_path.resolve()
+    args.map_dir = args.map_dir.resolve()
+    if args.output_dir is not None:
+        args.output_dir = args.output_dir.resolve()
+
+    if not args.policy_path.is_file():
+        raise FileNotFoundError(f"Policy checkpoint not found: {args.policy_path}")
+    if not args.map_dir.is_dir():
+        raise FileNotFoundError(f"Map directory not found: {args.map_dir}")
+
+    maps = list_maps(args.map_dir)
+    if not maps:
+        raise FileNotFoundError(f"No map_XXX.bin files found in {args.map_dir}")
+
+    output_dir = ensure_output_dir(args.output_dir, args.policy_path)
+    base_args = load_base_args(args)
+    seed_everything(args.seed)
+
+    # Find the controllable agents on each map.
+    discovery_cases, skipped_maps = discover_cases(base_args, maps)
+    if not discovery_cases:
+        raise RuntimeError("No controllable agents were discovered in the provided dataset.")
+
+    # Build eval cases and load the policy once against a matching env.
+    eval_cases = build_eval_cases(discovery_cases, args)
+    if not eval_cases:
+        raise RuntimeError("No evaluation cases were built for the selected eval modes.")
+    with open_case_env(base_args, eval_cases[0]) as env:
+        policy = create_policy(base_args, env)
+
+    # Run evaluation, then optionally rerender a sample of rollouts.
+    df = run_evaluations(base_args, policy, eval_cases, output_dir, args)
+    df, random_rendered, collision_rendered = maybe_render_videos(
+        base_args, policy, df, output_dir, args
+    )
+
+    summary = build_summary(df, maps, skipped_maps, output_dir, args)
+    summary["random_videos_rendered"] = random_rendered
+    summary["collision_videos_rendered"] = collision_rendered
+    (output_dir / "summary.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
+
+    print(f"Saved {len(df)} evaluation rows to {output_dir / 'results.csv'}")
+    print(json.dumps(summary, indent=2))
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description=__doc__)
+    add = parser.add_argument
+
+    add("--policy-path", type=Path, required=True, help="Path to the trained policy .pt checkpoint")
+    add("--map-dir", type=Path, required=True, help="Directory containing map_XXX.bin files")
+    add("--output-dir", type=Path, default=None, help="Directory to save dataframe artifacts and optional videos")
+    add("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="Torch device used for inference")
+    add(
+        "--override",
+        action="append",
+        default=[],
+        help="Config override in key=value or section.key=value form. Can be passed multiple times.",
+    )
+    add("--seed", type=int, default=42, help="Base seed for rollouts and random video sampling")
+    add(
+        "--eval-modes",
+        nargs="+",
+        choices=sorted(DEFAULT_EVAL_MODES),
+        default=list(DEFAULT_EVAL_MODES),
+        help="Which evaluation modes to run.",
+    )
+    add("--deterministic", action="store_true", help="Use argmax/mean actions instead of sampling")
+    add("--checkpoint-every", type=int, default=100, help="Write a partial CSV every N evaluated runs")
+    add("--num-random-videos", type=int, default=10, help="How many random rollout videos to render")
+    add("--num-collision-videos", type=int, default=10, help="How many colliding rollout videos to render if available")
+    add("--no-render-videos", action="store_true", help="Disable post-hoc video rendering")
+    add("--render-view", choices=sorted(RENDER_VIEW_MAP), default="full_sim_state")
+    add("--draw-traces", action="store_true", help="Draw logged replay traces in rerendered videos")
+    return parser.parse_args()
+
+
+def load_base_args(cli_args):
+    argv = sys.argv
+    sys.argv = [sys.argv[0]]
+    try:
+        args = load_config("puffer_drive")
+    finally:
+        sys.argv = argv
+
+    args["wandb"] = False
+    args["neptune"] = False
+    args["load_model_path"] = str(cli_args.policy_path)
+    args["train"]["device"] = cli_args.device
+    args["train"]["compile"] = False
+    args["env"]["render_mode"] = 1
+    args["env"]["init_mode"] = "create_all_valid"
+
+    for override in cli_args.override:
+        apply_override(args, override)
+
+    args["train"]["use_rnn"] = args["rnn_name"] is not None
+    return args
+
+
+def apply_override(config: dict, override: str):
+    if "=" not in override:
+        raise ValueError(f"Invalid override '{override}'. Expected key=value or section.key=value.")
+
+    path, raw = override.split("=", 1)
+    try:
+        value = ast.literal_eval(raw)
+    except (SyntaxError, ValueError):
+        value = raw
+
+    target = config
+    keys = path.split(".")
+    for key in keys[:-1]:
+        target = target[key]
+    target[keys[-1]] = value
+
+
+def list_maps(map_dir: Path) -> list[MapRecord]:
+    pattern = re.compile(r"map_(\d+)\.bin$")
+    maps = []
+    for path in sorted(map_dir.glob("map_*.bin")):
+        if match := pattern.match(path.name):
+            maps.append(MapRecord(int(match.group(1)), path.name, path.resolve()))
+    return maps
+
+
+def ensure_output_dir(base_dir: Path | None, policy_path: Path) -> Path:
+    if base_dir is None:
+        stamp = time.strftime("%Y%m%d_%H%M%S")
+        base_dir = Path("artifacts") / "policy_dataset_eval" / f"{policy_path.stem}_{stamp}"
+    base_dir.mkdir(parents=True, exist_ok=True)
+    return base_dir.resolve()
+
+
+def seed_everything(seed: int):
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+
+@contextlib.contextmanager
+def pushd(path: Path):
+    old_cwd = Path.cwd()
+    os.chdir(path)
+    try:
+        yield
+    finally:
+        os.chdir(old_cwd)
+
+
+def read_object_indices(source_path: Path) -> dict[int, int]:
+    data = source_path.read_bytes()
+    view = memoryview(data)
+    offset = SDC_TRACK_INDEX_OFFSET + 4
+
+    (num_tracks_to_predict,) = struct.unpack_from("i", view, offset)
+    offset += 4 + 4 * num_tracks_to_predict
+    num_objects, num_roads = struct.unpack_from("ii", view, offset)
+    offset += 8
+
+    object_id_to_index = {}
+    for entity_idx in range(num_objects + num_roads):
+        _, entity_type, entity_id, array_size = struct.unpack_from("iiii", view, offset)
+        offset += 16
+
+        if entity_idx < num_objects:
+            if entity_id in object_id_to_index:
+                raise ValueError(f"Duplicate object id {entity_id} in {source_path}")
+            object_id_to_index[entity_id] = entity_idx
+
+        offset += 12 * array_size
+        if entity_type in OBJECT_TYPES:
+            offset += 12 * array_size
+            offset += 4 * array_size
+            offset += 4 * array_size
+            offset += 20 * array_size
+        offset += 24
+        offset += 4
+
+    if offset != len(data):
+        raise ValueError(f"Unexpected binary layout while parsing {source_path}")
+    return object_id_to_index
+
+
+def patch_sdc_track_index(source_path: Path, output_path: Path, track_index: int):
+    data = bytearray(source_path.read_bytes())
+    struct.pack_into("i", data, SDC_TRACK_INDEX_OFFSET, int(track_index))
+    output_path.write_bytes(data)
+
+
+def symlink_or_copy(source_path: Path, output_path: Path):
+    try:
+        output_path.symlink_to(source_path)
+    except OSError:
+        shutil.copy2(source_path, output_path)
+
+
+@contextlib.contextmanager
+def staged_map_dir(source_path: Path, target_track_index: int | None = None):
+    with tempfile.TemporaryDirectory(prefix="drive_policy_eval_") as tmp:
+        staged_path = Path(tmp) / "map_000.bin"
+        if target_track_index is None:
+            symlink_or_copy(source_path, staged_path)
+        else:
+            patch_sdc_track_index(source_path, staged_path, target_track_index)
+        yield Path(tmp)
+
+
+def env_kwargs(base_args: dict, *, map_dir: Path, num_agents: int, control_mode: str, max_controlled_agents: int):
+    kwargs = dict(base_args["env"])
+    kwargs.update(
+        map_dir=str(map_dir),
+        num_maps=1,
+        num_agents=int(num_agents),
+        init_mode="create_all_valid",
+        control_mode=control_mode,
+        max_controlled_agents=int(max_controlled_agents),
+        report_interval=1,
+        render_mode=1,
+    )
+    return kwargs
+
+
+@contextlib.contextmanager
+def open_env(base_args: dict, *, map_dir: Path, num_agents: int, control_mode: str, max_controlled_agents: int):
+    with pushd(REPO_ROOT):
+        env = Drive(
+            **env_kwargs(
+                base_args,
+                map_dir=map_dir,
+                num_agents=num_agents,
+                control_mode=control_mode,
+                max_controlled_agents=max_controlled_agents,
+            )
+        )
+        try:
+            yield env
+        finally:
+            env.close()
+
+
+def case_env_spec(case: dict) -> dict:
+    if case["eval_mode"] == "human_replay":
+        return {
+            "target_track_index": int(case["target_track_index"]),
+            "num_agents": 1,
+            "control_mode": "control_sdc_only",
+            "max_controlled_agents": 1,
+        }
+    return {
+        "target_track_index": None,
+        "num_agents": int(case["num_policy_agents"]),
+        "control_mode": "control_agents",
+        "max_controlled_agents": int(case["num_policy_agents"]),
+    }
+
+
+@contextlib.contextmanager
+def open_case_env(base_args: dict, case: dict):
+    spec = case_env_spec(case)
+    with staged_map_dir(Path(case["source_map_path"]), spec["target_track_index"]) as map_dir:
+        with open_env(
+            base_args,
+            map_dir=map_dir,
+            num_agents=spec["num_agents"],
+            control_mode=spec["control_mode"],
+            max_controlled_agents=spec["max_controlled_agents"],
+        ) as env:
+            yield env
+
+
+def create_policy(base_args: dict, env: Drive):
+    wrapper = SimpleNamespace(driver_env=env)
+    try:
+        policy = load_policy(base_args, wrapper, "puffer_drive")
+    except RuntimeError as exc:
+        if base_args.get("rnn_name") is None and "lstm.weight_ih_l0" in str(exc):
+            print("Checkpoint appears recurrent; retrying with rnn_name=Recurrent.")
+            base_args["rnn_name"] = "Recurrent"
+            base_args["train"]["use_rnn"] = True
+            policy = load_policy(base_args, wrapper, "puffer_drive")
+        else:
+            raise
+    return policy.eval()
+
+
+def init_rnn_state(base_args: dict, env: Drive, policy):
+    if not base_args["train"]["use_rnn"]:
+        return {}
+    hidden_size = policy.hidden_size
+    device = base_args["train"]["device"]
+    return {
+        "lstm_h": torch.zeros(env.num_agents, hidden_size, device=device),
+        "lstm_c": torch.zeros(env.num_agents, hidden_size, device=device),
+    }
+
+
+def select_action(policy, obs, env: Drive, state: dict, device: str, deterministic: bool):
+    with torch.no_grad():
+        obs = torch.as_tensor(obs, device=device)
+        logits, _ = policy.forward_eval(obs, state)
+        if deterministic:
+            if isinstance(logits, torch.distributions.Normal):
+                action = logits.mean
+            else:
+                action = torch.stack([head.argmax(dim=-1) for head in logits], dim=-1)
+        else:
+            action, _, _ = pufferlib.pytorch.sample_logits(logits)
+
+    action = action.detach().cpu().numpy().reshape(env.action_space.shape)
+    if isinstance(logits, torch.distributions.Normal):
+        action = np.clip(action, env.action_space.low, env.action_space.high)
+    return action
+
+
+def validate_controlled_agents(env: Drive, expected_agent_ids: list[int]) -> list[int]:
+    controlled_ids = [int(agent_id) for agent_id in env.get_global_agent_state()["id"].tolist()]
+    if sorted(controlled_ids) != sorted(int(agent_id) for agent_id in expected_agent_ids):
+        raise RuntimeError(
+            f"Expected controlled agents {expected_agent_ids}, got {controlled_ids} "
+            f"for scenario {env.scenario_ids[0]}"
+        )
+    return controlled_ids
+
+
+def rollout_policy(
+    env: Drive,
+    policy,
+    base_args: dict,
+    *,
+    seed: int,
+    deterministic: bool,
+    expected_agent_ids: list[int],
+    render_view=None,
+    draw_traces=False,
+):
+    obs, _ = env.reset(seed=seed)
+    controlled_ids = validate_controlled_agents(env, expected_agent_ids)
+    state = init_rnn_state(base_args, env, policy)
+    final_info = None
+
+    for _ in range(env.episode_length):
+        if render_view is not None:
+            env.render(view_mode=render_view, draw_traces=draw_traces, env_id=0)
+
+        action = select_action(policy, obs, env, state, base_args["train"]["device"], deterministic)
+        obs, _, _, truncations, infos = env.step(action)
+        if infos:
+            final_info = infos[0]
+        if np.asarray(truncations).all():
+            break
+
+    if final_info is None:
+        raise RuntimeError(f"Rollout for scenario {env.scenario_ids[0]} finished without logs.")
+    return final_info, controlled_ids
+
+
+def discover_cases(base_args: dict, maps: list[MapRecord]):
+    discovery_cases = []
+    skipped_maps = []
+
+    for map_record in tqdm(maps, desc="Discovering maps", colour="cyan"):
+        try:
+            discovery_cases.extend(discover_controllable_agents(base_args, map_record))
+        except ValueError as exc:
+            skipped_maps.append(map_record.map_id)
+            print(f"Skipping {map_record.map_name}: {exc}")
+
+    return discovery_cases, skipped_maps
+
+
+def discover_controllable_agents(base_args: dict, map_record: MapRecord):
+    object_id_to_index = read_object_indices(map_record.source_path)
+
+    with staged_map_dir(map_record.source_path) as map_dir:
+        with open_env(
+            base_args,
+            map_dir=map_dir,
+            num_agents=binding.MAX_AGENTS,
+            control_mode="control_agents",
+            max_controlled_agents=binding.MAX_AGENTS,
+        ) as env:
+            env.reset(seed=0)
+            if env.num_envs < 1:
+                return []
+
+            state = env.get_global_agent_state()
+            first_env_count = env.agent_offsets[1] - env.agent_offsets[0]
+            scenario_id = env.scenario_ids[0]
+            cases = []
+
+            for rank in range(first_env_count):
+                agent_id = int(state["id"][rank])
+                if agent_id not in object_id_to_index:
+                    raise ValueError(
+                        f"Agent id {agent_id} from {map_record.map_name} was not found in the binary map"
+                    )
+
+                cases.append(
+                    {
+                        "map_id": map_record.map_id,
+                        "map_name": map_record.map_name,
+                        "source_map_path": str(map_record.source_path),
+                        "scenario_id": scenario_id,
+                        "policy_agent_id": agent_id,
+                        "target_track_index": int(object_id_to_index[agent_id]),
+                        "controllable_agent_rank": rank,
+                        "agent_length": float(state["length"][rank]),
+                        "agent_width": float(state["width"][rank]),
+                    }
+                )
+
+            return cases
+
+
+def build_self_play_cases(discovery_cases: list[dict]) -> list[dict]:
+    grouped = {}
+    for case in discovery_cases:
+        key = (int(case["map_id"]), str(case["source_map_path"]))
+        grouped.setdefault(key, []).append(case)
+
+    self_play_cases = []
+    for cases in grouped.values():
+        cases = sorted(cases, key=lambda case: int(case["controllable_agent_rank"]))
+        expected_agent_ids = [int(case["policy_agent_id"]) for case in cases]
+        self_play_cases.append(
+            {
+                "eval_mode": "self_play",
+                "map_id": int(cases[0]["map_id"]),
+                "map_name": cases[0]["map_name"],
+                "source_map_path": cases[0]["source_map_path"],
+                "scenario_id": cases[0]["scenario_id"],
+                "expected_agent_ids": expected_agent_ids,
+                "num_policy_agents": len(expected_agent_ids),
+            }
+        )
+
+    return sorted(self_play_cases, key=lambda case: int(case["map_id"]))
+
+
+def build_eval_cases(discovery_cases: list[dict], cli_args) -> list[dict]:
+    cases = []
+
+    if "human_replay" in cli_args.eval_modes:
+        cases.extend(
+            {
+                **case,
+                "eval_mode": "human_replay",
+                "expected_agent_ids": [int(case["policy_agent_id"])],
+                "deterministic": cli_args.deterministic,
+            }
+            for case in discovery_cases
+        )
+
+    if "self_play" in cli_args.eval_modes:
+        cases.extend(
+            {**case, "deterministic": cli_args.deterministic}
+            for case in build_self_play_cases(discovery_cases)
+        )
+
+    order = {mode: i for i, mode in enumerate(DEFAULT_EVAL_MODES)}
+    return sorted(
+        cases,
+        key=lambda case: (
+            int(case["map_id"]),
+            order.get(case["eval_mode"], len(order)),
+            int(case.get("controllable_agent_rank", -1)),
+        ),
+    )
+
+
+def evaluate_case(base_args: dict, policy, case: dict, run_seed: int):
+    with open_case_env(base_args, case) as env:
+        final_info, controlled_ids = rollout_policy(
+            env,
+            policy,
+            base_args,
+            seed=run_seed,
+            deterministic=case["deterministic"],
+            expected_agent_ids=list(case["expected_agent_ids"]),
+        )
+        scenario_id = env.scenario_ids[0]
+
+    row = {k: v for k, v in case.items() if k != "expected_agent_ids"}
+    row.update({key: float(final_info[key]) for key in LOG_METRIC_KEYS if key in final_info})
+    row["scenario_id"] = scenario_id
+    row["expected_agent_ids_json"] = json.dumps(list(case["expected_agent_ids"]))
+    row["controlled_agent_ids"] = json.dumps(controlled_ids)
+    row["num_policy_agents"] = len(controlled_ids)
+    row["had_collision"] = bool(
+        row.get("collision_rate", 0.0) > 0.0 or row.get("collisions_per_agent", 0.0) > 0.0
+    )
+    row["had_valid_collision"] = bool(row.get("collision_rate_valid", 0.0) > 0.0)
+    row["seed"] = run_seed
+    return row
+
+
+def run_evaluations(base_args: dict, policy, eval_cases: list[dict], output_dir: Path, cli_args):
+    rows = []
+
+    for run_idx, case in enumerate(tqdm(eval_cases, desc="Evaluating agents", colour="green"), start=1):
+        run_seed = cli_args.seed + run_idx
+        seed_everything(run_seed)
+        rows.append(evaluate_case(base_args, policy, case, run_seed))
+
+        if cli_args.checkpoint_every > 0 and run_idx % cli_args.checkpoint_every == 0:
+            write_results(pd.DataFrame(rows), output_dir, partial=True)
+
+    df = pd.DataFrame(rows)
+    sort_rank = df["controllable_agent_rank"].fillna(-1) if "controllable_agent_rank" in df else -1
+    df = df.assign(_sort_rank=sort_rank)
+    df = df.sort_values(["map_id", "eval_mode", "_sort_rank"]).drop(columns="_sort_rank").reset_index(drop=True)
+    df["random_video_path"] = None
+    df["collision_video_path"] = None
+
+    write_results(df, output_dir)
+    cleanup_partial_results(output_dir)
+    return df
+
+
+def make_case_mask(df: pd.DataFrame, case: dict):
+    mask = (df["map_id"] == int(case["map_id"])) & (df["eval_mode"] == str(case["eval_mode"]))
+    if case["eval_mode"] == "human_replay":
+        mask &= df["policy_agent_id"] == int(case["policy_agent_id"])
+    return mask
+
+
+def sanitize_name(value: str) -> str:
+    return re.sub(r"[^A-Za-z0-9._-]+", "_", value.strip()).strip("_") or "unknown"
+
+
+def build_render_filename(case: dict, idx: int):
+    name = (
+        f"{idx:02d}_mode_{case['eval_mode']}"
+        f"__map_{int(case['map_id']):03d}"
+        f"__scenario_{sanitize_name(str(case['scenario_id']))}"
+    )
+    if case["eval_mode"] == "human_replay":
+        name += f"__agent_{int(case['policy_agent_id'])}"
+    else:
+        name += f"__agents_{int(case['num_policy_agents'])}"
+    return f"{name}.mp4"
+
+
+def render_case(base_args: dict, policy, case: dict, output_path: Path, render_view, draw_traces: bool):
+    render_file = REPO_ROOT / f"{case['scenario_id']}.mp4"
+    if render_file.exists():
+        render_file.unlink()
+
+    try:
+        seed_everything(int(case["seed"]))
+        with open_case_env(base_args, case) as env:
+            rollout_policy(
+                env,
+                policy,
+                base_args,
+                seed=int(case["seed"]),
+                deterministic=bool(case["deterministic"]),
+                expected_agent_ids=json.loads(case["expected_agent_ids_json"]),
+                render_view=render_view,
+                draw_traces=draw_traces,
+            )
+
+        if not render_file.exists():
+            if case["eval_mode"] == "human_replay":
+                detail = f"agent {case['policy_agent_id']}"
+            else:
+                detail = f"{int(case['num_policy_agents'])} self-play agents"
+            raise RuntimeError(f"No mp4 was produced while rendering {case['scenario_id']} ({detail})")
+
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        if output_path.exists():
+            output_path.unlink()
+        shutil.move(str(render_file), output_path)
+        return output_path
+    finally:
+        if render_file.exists():
+            render_file.unlink()
+
+
+def maybe_render_videos(base_args: dict, policy, df: pd.DataFrame, output_dir: Path, cli_args):
+    if cli_args.no_render_videos:
+        return df, 0, 0
+    if shutil.which("ffmpeg") is None:
+        print("Skipping video rendering because ffmpeg is not installed.")
+        return df, 0, 0
+
+    render_view = RENDER_VIEW_MAP[cli_args.render_view]
+    random_cases, collision_cases = choose_render_cases(df, cli_args)
+    random_rendered = 0
+    collision_rendered = 0
+
+    for category, cases in [("random", random_cases), ("collision", collision_cases)]:
+        for idx, case in enumerate(tqdm(cases, desc=f"Rendering {category} videos", colour="magenta"), start=1):
+            output_path = output_dir / "videos" / case["eval_mode"] / category / build_render_filename(case, idx)
+            render_case(base_args, policy, case, output_path, render_view, cli_args.draw_traces)
+
+            column = "random_video_path" if category == "random" else "collision_video_path"
+            df.loc[make_case_mask(df, case), column] = str(output_path.relative_to(output_dir))
+            if category == "random":
+                random_rendered += 1
+            else:
+                collision_rendered += 1
+
+    write_results(df, output_dir)
+    return df, random_rendered, collision_rendered
+
+
+def choose_render_cases(df: pd.DataFrame, cli_args):
+    rng = random.Random(cli_args.seed)
+    random_cases = rng.sample(df.to_dict("records"), min(cli_args.num_random_videos, len(df)))
+
+    collision_cases = df[df["had_collision"]].to_dict("records")
+    collision_cases = rng.sample(collision_cases, min(cli_args.num_collision_videos, len(collision_cases)))
+    return random_cases, collision_cases
+
+
+def write_results(df: pd.DataFrame, output_dir: Path, partial: bool = False):
+    stem = "results.partial" if partial else "results"
+    df.to_csv(output_dir / f"{stem}.csv", index=False)
+    df.to_pickle(output_dir / f"{stem}.pkl")
+
+    if not partial and "eval_mode" in df:
+        for eval_mode in sorted(df["eval_mode"].dropna().unique()):
+            mode_df = df[df["eval_mode"] == eval_mode].reset_index(drop=True)
+            mode_df.to_csv(output_dir / f"{eval_mode}_results.csv", index=False)
+            mode_df.to_pickle(output_dir / f"{eval_mode}_results.pkl")
+
+
+def cleanup_partial_results(output_dir: Path):
+    for path in [output_dir / "results.partial.csv", output_dir / "results.partial.pkl"]:
+        if path.exists():
+            path.unlink()
+
+
+def metric_summary(df: pd.DataFrame) -> dict:
+    summary = {
+        "num_runs": int(len(df)),
+        "num_collisions": int(df["had_collision"].sum()) if "had_collision" in df else 0,
+        "num_valid_collisions": int(df["had_valid_collision"].sum()) if "had_valid_collision" in df else 0,
+    }
+    for column in [*LOG_METRIC_KEYS, "had_collision", "had_valid_collision"]:
+        if column in df:
+            summary[f"mean/{column}"] = float(df[column].mean())
+    return summary
+
+
+def build_summary(df: pd.DataFrame, maps: list[MapRecord], skipped_maps: list[int], output_dir: Path, cli_args):
+    summary = {
+        "policy_path": str(cli_args.policy_path),
+        "map_dir": str(cli_args.map_dir),
+        "output_dir": str(output_dir),
+        "eval_modes": list(cli_args.eval_modes),
+        "num_maps_requested": len(maps),
+        "num_maps_skipped": len(skipped_maps),
+        "skipped_map_ids": skipped_maps,
+        **metric_summary(df),
+        "modes": {},
+    }
+
+    if "eval_mode" in df:
+        for eval_mode in sorted(df["eval_mode"].dropna().unique()):
+            summary["modes"][eval_mode] = metric_summary(df[df["eval_mode"] == eval_mode])
+
+    return summary
+
+
+if __name__ == "__main__":
+    main()