add rl benchmark

benchmark/__init__.py

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+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+from __future__ import annotations
+
+from .runner import BenchmarkRunner
+
+__all__ = ["BenchmarkRunner"]

benchmark/algorithms/__init__.py

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+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------

benchmark/algorithms/grpo.yaml

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+name: grpo
+config:
+  policy:
+    name: actor_only
+    actor:
+      type: mlp
+      network_cfg:
+        hidden_sizes: [256, 256]
+        activation: relu
+  algorithm:
+    name: grpo
+    cfg:
+      learning_rate: 0.0001
+      n_epochs: 10
+      batch_size: 8192
+      gamma: 0.99
+      clip_coef: 0.2
+      ent_coef: 0.01
+      kl_coef: 0.0
+      group_size: 4
+      eps: 1.0e-8
+      reset_every_rollout: true
+      truncate_at_first_done: true
+      max_grad_norm: 0.5

benchmark/algorithms/ppo.yaml

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+name: ppo
+config:
+  policy:
+    name: actor_critic
+    actor:
+      type: mlp
+      network_cfg:
+        hidden_sizes: [256, 256]
+        activation: relu
+    critic:
+      type: mlp
+      network_cfg:
+        hidden_sizes: [256, 256]
+        activation: relu
+  algorithm:
+    name: ppo
+    cfg:
+      learning_rate: 0.0001
+      n_epochs: 10
+      batch_size: 8192
+      gamma: 0.99
+      gae_lambda: 0.95
+      clip_coef: 0.2
+      ent_coef: 0.01
+      vf_coef: 0.5
+      max_grad_norm: 0.5

benchmark/config.py

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+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+from __future__ import annotations
+
+from copy import deepcopy
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+
+BENCHMARK_ROOT = Path(__file__).resolve().parent
+
+
+def load_yaml(path: str | Path) -> dict[str, Any]:
+    """Load a YAML file into a dictionary."""
+    with Path(path).open("r", encoding="utf-8") as file:
+        data = yaml.safe_load(file) or {}
+    if not isinstance(data, dict):
+        raise TypeError(f"Expected mapping in YAML file {path}, got {type(data)!r}.")
+    return data
+
+
+def deep_update(base: dict[str, Any], override: dict[str, Any]) -> dict[str, Any]:
+    """Recursively merge `override` into `base` and return a new mapping."""
+    merged = deepcopy(base)
+    for key, value in override.items():
+        if (
+            key in merged
+            and isinstance(merged[key], dict)
+            and isinstance(value, dict)
+        ):
+            merged[key] = deep_update(merged[key], value)
+        else:
+            merged[key] = deepcopy(value)
+    return merged
+
+
+def load_task_spec(name: str) -> dict[str, Any]:
+    """Load a benchmark task specification by name."""
+    return load_yaml(BENCHMARK_ROOT / "tasks" / f"{name}.yaml")
+
+
+def load_algorithm_spec(name: str) -> dict[str, Any]:
+    """Load a benchmark algorithm specification by name."""
+    return load_yaml(BENCHMARK_ROOT / "algorithms" / f"{name}.yaml")
+
+
+def load_suite_spec(name: str = "default") -> dict[str, Any]:
+    """Load a benchmark suite specification by name."""
+    return load_yaml(BENCHMARK_ROOT / "suites" / f"{name}.yaml")
+
+
+__all__ = [
+    "BENCHMARK_ROOT",
+    "deep_update",
+    "load_algorithm_spec",
+    "load_suite_spec",
+    "load_task_spec",
+    "load_yaml",
+]