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sms.py
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1359 lines (1248 loc) · 96.1 KB
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import os
import pickle
import json
from argparse import Namespace
import numpy as np
import matplotlib.pyplot as plt
import torch
from utils.evaluation import load_trained_network
from utils.state_machine_analysis import gen_ref_trajectories_by_bayes, gen_metaRL_trajectories_given_ref
from utils.state_machine_analysis import gen_state_mapper_training_dataset
from utils.state_machine_analysis import get_PCA_model, plot_PCA_bayes_and_metaRL
from utils.state_machine_analysis import train_state_space_mapper
from utils.state_machine_analysis import get_mapped_bayes_states_and_actions
from utils.state_machine_analysis import get_mapped_metaRL_states_and_actions
from utils.state_machine_analysis import dissimilarity_analysis
if __name__ == "__main__":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
trained_model_paths = [
# add the paths to the trained models you want to analyze
]
for trained_model_path in trained_model_paths:
print(f'Trained_model_path: {trained_model_path}')
# for document
lines = []
lines.append(f'trained_model_path: {trained_model_path}')
# for saving ana result
results = {}
# -- LOAD --
# load args
args_json_path = os.path.join(trained_model_path, 'config.json')
with open(args_json_path, 'rt') as f_json:
args_dict = json.load(f_json)
args = Namespace(**args_dict)
# load trained model
if args.exp_label == 'rl2':
load_a2crnn_path = os.path.join(trained_model_path, 'actor_critic_weights.h5')
a2crnn = load_trained_network(
network_type='a2crnn',
path_to_trained_network_state_dict=load_a2crnn_path,
args=args,
device=device
)
elif args.exp_label == 'mpc':
load_a2cmlp_path = os.path.join(trained_model_path, 'actor_critic_weights.h5')
a2cmlp = load_trained_network(
network_type='a2cmlp',
path_to_trained_network_state_dict=load_a2cmlp_path,
args=args,
device=device
)
load_encoder_path = os.path.join(trained_model_path, 'encoder_weights.h5')
rnn_encoder = load_trained_network(
network_type='rnn_encoder',
path_to_trained_network_state_dict=load_encoder_path,
args=args,
device=device
)
load_reward_decoder_path = os.path.join(trained_model_path, 'reward_decoder_weights.h5')
reward_decoder = load_trained_network(
network_type='reward_decoder',
path_to_trained_network_state_dict=load_reward_decoder_path,
args=args,
device=device
)
# load untrained model
if args.exp_label == 'rl2':
load_a2crnn_path = os.path.join(trained_model_path, 'actor_critic_weights-1.h5')
untrained_a2crnn = load_trained_network(
network_type='a2crnn',
path_to_trained_network_state_dict=load_a2crnn_path,
args=args,
device=device
)
elif args.exp_label == 'mpc':
load_a2cmlp_path = os.path.join(trained_model_path, 'actor_critic_weights-1.h5')
untrained_a2cmlp = load_trained_network(
network_type='a2cmlp',
path_to_trained_network_state_dict=load_a2cmlp_path,
args=args,
device=device
)
load_encoder_path = os.path.join(trained_model_path, 'encoder_weights-1.h5')
untrained_rnn_encoder = load_trained_network(
network_type='rnn_encoder',
path_to_trained_network_state_dict=load_encoder_path,
args=args,
device=device
)
load_reward_decoder_path = os.path.join(trained_model_path, 'reward_decoder_weights-1.h5')
untrained_reward_decoder = load_trained_network(
network_type='reward_decoder',
path_to_trained_network_state_dict=load_reward_decoder_path,
args=args,
device=device
)
# ------------------------------------------------------------------
# -- STATE MACHINE ANALYSIS --
# ------------------------------------------------------------------
save_dir = trained_model_path
# hyper param
sma_total_trials = 40
sma_env_name = args.env_name
sma_num_training_envs = 500
sma_num_testing_envs = 200
sma_num_training_epochs = 500
sma_training_batch_size = 64
sma_validation_split = 0.2
sma_patience = 10
sma_min_delta = 0.01
sma_min_training_epochs = 0
sma_num_bandits = 2 # action_dim
biased_beta_prior = None
bayes_state_dim = sma_num_bandits * 2 # dim of bayes belief state, depending on the task
if args.exp_label == 'mpc':
encoder = rnn_encoder
policy_network = a2cmlp
untrained_encoder = untrained_rnn_encoder
untrained_policy_network = untrained_a2cmlp
metaRL_rnn_state_dim = args.encoder_rnn_hidden_dim
metaRL_belief_state_dim = args.latent_dim * 2
elif args.exp_label == 'rl2':
encoder = None
policy_network = a2crnn
untrained_encoder = None
untrained_policy_network = untrained_a2crnn
metaRL_rnn_state_dim = args.rnn_hidden_dim
if len(args.layers_after_rnn) > 0:
metaRL_bottleneck_state_dim = args.layers_after_rnn[0]
else:
raise ValueError(f'incompatible model type: {args.exp_label}')
# get optimal solver
# if latent goal cart
from utils.bayes_optimal_agents import LatentGoalCartPOMDPSolver, LatentGoalCartBeliefBasedAgent
from utils.bayes_optimal_agents import train_latent_goal_cart_solver
solver = LatentGoalCartPOMDPSolver(env=None, belief_resolution=101, pos_resolution=41, gamma=0.95)
solver = train_latent_goal_cart_solver(solver)
latent_cart_optimal_agent = LatentGoalCartBeliefBasedAgent(solver)
# generate training dataset for state mapping
ref_trajectories_bayes_train_bayes_sampled = gen_ref_trajectories_by_bayes(
env_name=sma_env_name,
total_trials=sma_total_trials,
biased_beta_prior=biased_beta_prior,
num_envs=sma_num_training_envs,
num_bandits=sma_num_bandits
) # 1 extra trial to roll out one final step
conditioned_trajectories_metaRL_train_bayes_sampled = gen_metaRL_trajectories_given_ref(
encoder=encoder,
policy_network=policy_network,
args=args,
ref_actions=ref_trajectories_bayes_train_bayes_sampled['bayes_actions'][:, :-1],
ref_rewards=ref_trajectories_bayes_train_bayes_sampled['bayes_rewards'][:, :-1]
)
conditioned_trajectories_untrained_metaRL_train_bayes_sampled = gen_metaRL_trajectories_given_ref(
encoder=untrained_encoder,
policy_network=untrained_policy_network,
args=args,
ref_actions=ref_trajectories_bayes_train_bayes_sampled['bayes_actions'][:, :-1],
ref_rewards=ref_trajectories_bayes_train_bayes_sampled['bayes_rewards'][:, :-1]
)
# save training dataset
with open(os.path.join(trained_model_path, 'sma_ref_trajectories_bayes_train_bayes_sampled.pickle'), 'wb') as fo:
pickle.dump(ref_trajectories_bayes_train_bayes_sampled, fo)
with open(os.path.join(trained_model_path, 'sma_conditioned_trajectories_metaRL_train_bayes_sampled.pickle'), 'wb') as fo:
pickle.dump(conditioned_trajectories_metaRL_train_bayes_sampled, fo)
with open(os.path.join(trained_model_path, 'sma_conditioned_trajectories_untrained_metaRL_train_bayes_sampled.pickle'), 'wb') as fo:
pickle.dump(conditioned_trajectories_untrained_metaRL_train_bayes_sampled, fo)
# flatten for state space
flattened_bayes_states_train_bayes_sampled = ref_trajectories_bayes_train_bayes_sampled['bayes_states'][:, :-1, :].\
reshape(-1, bayes_state_dim)
flattened_metaRL_rnn_states_train_bayes_sampled = conditioned_trajectories_metaRL_train_bayes_sampled['metaRL_rnn_states'].\
reshape(-1, metaRL_rnn_state_dim)
flattened_untrained_metaRL_rnn_states_train_bayes_sampled = conditioned_trajectories_untrained_metaRL_train_bayes_sampled['metaRL_rnn_states'].\
reshape(-1, metaRL_rnn_state_dim)
flattened_bayes_actions_train_bayes_sampled = ref_trajectories_bayes_train_bayes_sampled['bayes_actions'][:, :-1].\
reshape(-1, 1)
flattened_metaRL_all_action_logits_train_bayes_sampled = conditioned_trajectories_metaRL_train_bayes_sampled['metaRL_all_action_logits'].\
reshape(-1, sma_num_bandits)
flattened_metaRL_a1_probs_train_bayes_sampled = conditioned_trajectories_metaRL_train_bayes_sampled['metaRL_all_action_probs'][:, :, 1].\
reshape(-1, 1)
flattened_untrained_metaRL_all_action_logits_train_bayes_sampled = conditioned_trajectories_untrained_metaRL_train_bayes_sampled['metaRL_all_action_logits'].\
reshape(-1, sma_num_bandits)
flattened_untrained_metaRL_a1_probs_train_bayes_sampled = conditioned_trajectories_untrained_metaRL_train_bayes_sampled['metaRL_all_action_probs'][:, :, 1].\
reshape(-1, 1)
if args.exp_label == 'rl2':
if len(args.layers_after_rnn) > 0: # if bottleneck layer exists
flattened_metaRL_bottleneck_states_train_bayes_sampled = conditioned_trajectories_metaRL_train_bayes_sampled['metaRL_bottleneck_states'].\
reshape(-1, metaRL_bottleneck_state_dim)
flattened_untrained_metaRL_bottleneck_states_train_bayes_sampled = conditioned_trajectories_untrained_metaRL_train_bayes_sampled['metaRL_bottleneck_states'].\
reshape(-1, metaRL_bottleneck_state_dim)
if args.exp_label == 'mpc':
flattened_metaRL_belief_states_train_bayes_sampled = conditioned_trajectories_metaRL_train_bayes_sampled['metaRL_belief_states'].\
reshape(-1, metaRL_belief_state_dim)
flattened_metaRL_belief_states_mean_only_train_bayes_sampled = conditioned_trajectories_metaRL_train_bayes_sampled['metaRL_belief_states_mean_only'].\
reshape(-1, round(metaRL_belief_state_dim/2))
flattened_untrained_metaRL_belief_states_train_bayes_sampled = conditioned_trajectories_untrained_metaRL_train_bayes_sampled['metaRL_belief_states'].\
reshape(-1, metaRL_belief_state_dim)
flattened_untrained_metaRL_belief_states_mean_only_train_bayes_sampled = conditioned_trajectories_untrained_metaRL_train_bayes_sampled['metaRL_belief_states_mean_only'].\
reshape(-1, round(metaRL_belief_state_dim/2))
# get PCA models
pca_model_bayes_states_bayes_sampled = get_PCA_model(flattened_bayes_states_train_bayes_sampled, bayes_state_dim)
pca_model_metaRL_rnn_states_bayes_sampled = get_PCA_model(flattened_metaRL_rnn_states_train_bayes_sampled, metaRL_rnn_state_dim)
pca_model_untrained_metaRL_rnn_states_bayes_sampled = get_PCA_model(flattened_untrained_metaRL_rnn_states_train_bayes_sampled, metaRL_rnn_state_dim)
if args.exp_label == 'rl2':
if len(args.layers_after_rnn) > 0: # if bottleneck layer exists
pca_model_metaRL_bottleneck_states_bayes_sampled = get_PCA_model(flattened_metaRL_bottleneck_states_train_bayes_sampled, metaRL_bottleneck_state_dim)
pca_model_untrained_metaRL_bottleneck_states_bayes_sampled = get_PCA_model(flattened_untrained_metaRL_bottleneck_states_train_bayes_sampled, metaRL_bottleneck_state_dim)
if args.exp_label == 'mpc':
pca_model_metaRL_belief_states_bayes_sampled = get_PCA_model(flattened_metaRL_belief_states_train_bayes_sampled, metaRL_belief_state_dim)
pca_model_untrained_metaRL_belief_states_bayes_sampled = get_PCA_model(flattened_untrained_metaRL_belief_states_train_bayes_sampled, metaRL_belief_state_dim)
pca_model_metaRL_belief_states_mean_only_bayes_sampled = get_PCA_model(flattened_metaRL_belief_states_mean_only_train_bayes_sampled, round(metaRL_belief_state_dim/2))
pca_model_untrained_metaRL_belief_states_mean_only_bayes_sampled = get_PCA_model(flattened_untrained_metaRL_belief_states_mean_only_train_bayes_sampled, round(metaRL_belief_state_dim/2))
# plot PCA training set
fig_sma_pca_train_metaRL_rnn_states_bayes_sampled = plot_PCA_bayes_and_metaRL(
pca_model_bayes=pca_model_bayes_states_bayes_sampled,
pca_model_metaRL=pca_model_metaRL_rnn_states_bayes_sampled,
true_bayes_states=flattened_bayes_states_train_bayes_sampled,
mapped_bayes_states=None,
true_metaRL_states=flattened_metaRL_rnn_states_train_bayes_sampled,
mapped_metaRL_states=None,
true_bayes_actions=flattened_bayes_actions_train_bayes_sampled,
mapped_bayes_actions=None,
env_name=sma_env_name,
true_metaRL_a1_probs=flattened_metaRL_a1_probs_train_bayes_sampled,
mapped_metaRL_a1_probs=None,
true_metaRL_all_action_logits=flattened_metaRL_all_action_logits_train_bayes_sampled,
mapped_metaRL_all_action_logits=None,
total_trials=sma_total_trials,
num_bandits=sma_num_bandits
)
fig_sma_pca_train_metaRL_rnn_states_bayes_sampled.savefig(os.path.join(trained_model_path, 'fig_sma_pca_train_metaRL_rnn_states_bayes_sampled.png'))
fig_sma_pca_train_untrained_metaRL_rnn_states_bayes_sampled = plot_PCA_bayes_and_metaRL(
pca_model_bayes=pca_model_bayes_states_bayes_sampled,
pca_model_metaRL=pca_model_untrained_metaRL_rnn_states_bayes_sampled,
true_bayes_states=flattened_bayes_states_train_bayes_sampled,
mapped_bayes_states=None,
true_metaRL_states=flattened_untrained_metaRL_rnn_states_train_bayes_sampled,
mapped_metaRL_states=None,
true_bayes_actions=flattened_bayes_actions_train_bayes_sampled,
mapped_bayes_actions=None,
env_name=sma_env_name,
true_metaRL_a1_probs=flattened_untrained_metaRL_a1_probs_train_bayes_sampled,
mapped_metaRL_a1_probs=None,
true_metaRL_all_action_logits=flattened_untrained_metaRL_all_action_logits_train_bayes_sampled,
mapped_metaRL_all_action_logits=None,
total_trials=sma_total_trials,
num_bandits=sma_num_bandits
)
fig_sma_pca_train_untrained_metaRL_rnn_states_bayes_sampled.savefig(os.path.join(trained_model_path, 'fig_sma_pca_train_untrained_metaRL_rnn_states_bayes_sampled.png'))
if args.exp_label == 'rl2':
if len(args.layers_after_rnn) > 0: # if bottleneck layer exists
fig_sma_pca_train_metaRL_bottleneck_states_bayes_sampled = plot_PCA_bayes_and_metaRL(
pca_model_bayes=pca_model_bayes_states_bayes_sampled,
pca_model_metaRL=pca_model_metaRL_bottleneck_states_bayes_sampled,
true_bayes_states=flattened_bayes_states_train_bayes_sampled,
mapped_bayes_states=None,
true_metaRL_states=flattened_metaRL_bottleneck_states_train_bayes_sampled,
mapped_metaRL_states=None,
true_bayes_actions=flattened_bayes_actions_train_bayes_sampled,
mapped_bayes_actions=None,
env_name=sma_env_name,
true_metaRL_a1_probs=flattened_metaRL_a1_probs_train_bayes_sampled,
mapped_metaRL_a1_probs=None,
true_metaRL_all_action_logits=flattened_metaRL_all_action_logits_train_bayes_sampled,
mapped_metaRL_all_action_logits=None,
total_trials=sma_total_trials,
num_bandits=sma_num_bandits
)
fig_sma_pca_train_metaRL_bottleneck_states_bayes_sampled.savefig(os.path.join(trained_model_path, 'fig_sma_pca_train_metaRL_bottleneck_states_bayes_sampled.png'))
fig_sma_pca_train_untrained_metaRL_bottleneck_states_bayes_sampled = plot_PCA_bayes_and_metaRL(
pca_model_bayes=pca_model_bayes_states_bayes_sampled,
pca_model_metaRL=pca_model_untrained_metaRL_bottleneck_states_bayes_sampled,
true_bayes_states=flattened_bayes_states_train_bayes_sampled,
mapped_bayes_states=None,
true_metaRL_states=flattened_untrained_metaRL_bottleneck_states_train_bayes_sampled,
mapped_metaRL_states=None,
true_bayes_actions=flattened_bayes_actions_train_bayes_sampled,
mapped_bayes_actions=None,
env_name=sma_env_name,
true_metaRL_a1_probs=flattened_untrained_metaRL_a1_probs_train_bayes_sampled,
mapped_metaRL_a1_probs=None,
true_metaRL_all_action_logits=flattened_untrained_metaRL_all_action_logits_train_bayes_sampled,
mapped_metaRL_all_action_logits=None,
total_trials=sma_total_trials,
num_bandits=sma_num_bandits
)
fig_sma_pca_train_untrained_metaRL_bottleneck_states_bayes_sampled.savefig(os.path.join(trained_model_path, 'fig_sma_pca_train_untrained_metaRL_bottleneck_states_bayes_sampled.png'))
if args.exp_label == 'mpc':
fig_sma_pca_train_metaRL_belief_states_bayes_sampled = plot_PCA_bayes_and_metaRL(
pca_model_bayes=pca_model_bayes_states_bayes_sampled,
pca_model_metaRL=pca_model_metaRL_belief_states_mean_only_bayes_sampled,
true_bayes_states=flattened_bayes_states_train_bayes_sampled,
mapped_bayes_states=None,
true_metaRL_states=flattened_metaRL_belief_states_mean_only_train_bayes_sampled,
mapped_metaRL_states=None,
true_bayes_actions=flattened_bayes_actions_train_bayes_sampled,
mapped_bayes_actions=None,
env_name=sma_env_name,
true_metaRL_a1_probs=flattened_metaRL_a1_probs_train_bayes_sampled,
mapped_metaRL_a1_probs=None,
true_metaRL_all_action_logits=flattened_metaRL_all_action_logits_train_bayes_sampled,
mapped_metaRL_all_action_logits=None,
total_trials=sma_total_trials,
num_bandits=sma_num_bandits
)
fig_sma_pca_train_metaRL_belief_states_bayes_sampled.savefig(os.path.join(trained_model_path, 'fig_sma_pca_train_metaRL_belief_states_bayes_sampled.png'))
fig_sma_pca_train_untrained_metaRL_belief_states_bayes_sampled = plot_PCA_bayes_and_metaRL(
pca_model_bayes=pca_model_bayes_states_bayes_sampled,
pca_model_metaRL=pca_model_untrained_metaRL_belief_states_mean_only_bayes_sampled,
true_bayes_states=flattened_bayes_states_train_bayes_sampled,
mapped_bayes_states=None,
true_metaRL_states=flattened_untrained_metaRL_belief_states_mean_only_train_bayes_sampled,
mapped_metaRL_states=None,
true_bayes_actions=flattened_bayes_actions_train_bayes_sampled,
mapped_bayes_actions=None,
env_name=sma_env_name,
true_metaRL_a1_probs=flattened_untrained_metaRL_a1_probs_train_bayes_sampled,
mapped_metaRL_a1_probs=None,
true_metaRL_all_action_logits=flattened_untrained_metaRL_all_action_logits_train_bayes_sampled,
mapped_metaRL_all_action_logits=None,
total_trials=sma_total_trials,
num_bandits=sma_num_bandits
)
fig_sma_pca_train_untrained_metaRL_belief_states_bayes_sampled.savefig(os.path.join(trained_model_path, 'fig_sma_pca_train_untrained_metaRL_belief_states_bayes_sampled.png'))
# pack dataset 1: without first PCA'ed
metaRL_rnn2bayes_dataset_bayes_sampled, bayes2metaRL_rnn_dataset_bayes_sampled = \
gen_state_mapper_training_dataset(
flattened_metaRL_states=flattened_metaRL_rnn_states_train_bayes_sampled,
flattened_bayes_states=flattened_bayes_states_train_bayes_sampled
)
untrained_metaRL_rnn2bayes_dataset_bayes_sampled, bayes2untrained_metaRL_rnn_dataset_bayes_sampled = \
gen_state_mapper_training_dataset(
flattened_metaRL_states=flattened_untrained_metaRL_rnn_states_train_bayes_sampled,
flattened_bayes_states=flattened_bayes_states_train_bayes_sampled
)
if args.exp_label == 'rl2':
if len(args.layers_after_rnn) > 0: # if bottleneck layer exists
metaRL_bottleneck2bayes_dataset_bayes_sampled, bayes2metaRL_bottleneck_dataset_bayes_sampled = \
gen_state_mapper_training_dataset(
flattened_metaRL_states=flattened_metaRL_bottleneck_states_train_bayes_sampled,
flattened_bayes_states=flattened_bayes_states_train_bayes_sampled
)
untrained_metaRL_bottleneck2bayes_dataset_bayes_sampled, bayes2untrained_metaRL_bottleneck_dataset_bayes_sampled = \
gen_state_mapper_training_dataset(
flattened_metaRL_states=flattened_untrained_metaRL_bottleneck_states_train_bayes_sampled,
flattened_bayes_states=flattened_bayes_states_train_bayes_sampled
)
if args.exp_label == 'mpc':
metaRL_belief2bayes_dataset_bayes_sampled, bayes2metaRL_belief_dataset_bayes_sampled = \
gen_state_mapper_training_dataset(
flattened_metaRL_states=flattened_metaRL_belief_states_train_bayes_sampled,
flattened_bayes_states=flattened_bayes_states_train_bayes_sampled
)
untrained_metaRL_belief2bayes_dataset_bayes_sampled, bayes2untrained_metaRL_belief_dataset_bayes_sampled = \
gen_state_mapper_training_dataset(
flattened_metaRL_states=flattened_untrained_metaRL_belief_states_train_bayes_sampled,
flattened_bayes_states=flattened_bayes_states_train_bayes_sampled
)
# ------------------------------------------------------------------
# training state space mapper: for dataset 1, without first PCA'ed
# ------------------------------------------------------------------
# for trained_metaRL_rnn
## metaRL_rnn2bayes_bayes_sampled
source_dim = metaRL_rnn_state_dim
target_dim = bayes_state_dim
dataset = metaRL_rnn2bayes_dataset_bayes_sampled
metaRL_rnn2bayes_mapper_bayes_sampled, losses_mse_metaRL_rnn2bayes_bayes_sampled = \
train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
metaRL_rnn2bayes_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_metaRL_rnn2bayes_bayes_sampled_weights.h5')
torch.save(metaRL_rnn2bayes_mapper_bayes_sampled.state_dict(), metaRL_rnn2bayes_mapper_bayes_sampled_save_path)
bayes_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_bayes_states_train_bayes_sampled), axis=1))
mse_metaRL_rnn2bayes_bayes_sampled = losses_mse_metaRL_rnn2bayes_bayes_sampled[-1]
normalized_mse_metaRL_rnn2bayes_bayes_sampled = mse_metaRL_rnn2bayes_bayes_sampled/ bayes_avg_var_bayes_sampled
print(f'sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
print(f'sma_mse_metaRL_rnn2bayes: {mse_metaRL_rnn2bayes_bayes_sampled}')
print(f'sma_normalized_mse_metaRL_rnn2bayes: {normalized_mse_metaRL_rnn2bayes_bayes_sampled}')
lines.append('state mapper training: trained_metaRL_rnn')
lines.append(f' sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
lines.append(f' sma_mse_metaRL_rnn2bayes: {mse_metaRL_rnn2bayes_bayes_sampled}')
lines.append(f' sma_normalized_mse_metaRL_rnn2bayes: {normalized_mse_metaRL_rnn2bayes_bayes_sampled}')
## bayes2metaRL_rnn_bayes_sampled
source_dim = bayes_state_dim
target_dim = metaRL_rnn_state_dim
dataset = bayes2metaRL_rnn_dataset_bayes_sampled
bayes2metaRL_rnn_mapper_bayes_sampled, losses_mse_bayes2metaRL_rnn_bayes_sampled = \
train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
bayes2metaRL_rnn_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_bayes2metaRL_rnn_bayes_sampled_weights.h5')
torch.save(bayes2metaRL_rnn_mapper_bayes_sampled.state_dict(), bayes2metaRL_rnn_mapper_bayes_sampled_save_path)
metaRL_rnn_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_metaRL_rnn_states_train_bayes_sampled), axis=1))
mse_bayes2metaRL_rnn_bayes_sampled = losses_mse_bayes2metaRL_rnn_bayes_sampled[-1]
normalized_mse_bayes2metaRL_rnn_bayes_sampled = mse_bayes2metaRL_rnn_bayes_sampled/ metaRL_rnn_avg_var_bayes_sampled
print(f'sma_metaRL_rnn_avg_var: {metaRL_rnn_avg_var_bayes_sampled}')
print(f'sma_mse_bayes2metaRL_rnn: {mse_bayes2metaRL_rnn_bayes_sampled}')
print(f'sma_normalized_mse_bayes2metaRL_rnn: {normalized_mse_bayes2metaRL_rnn_bayes_sampled}')
lines.append(f' sma_metaRL_rnn_avg_var: {metaRL_rnn_avg_var_bayes_sampled}')
lines.append(f' sma_mse_bayes2metaRL_rnn: {mse_bayes2metaRL_rnn_bayes_sampled}')
lines.append(f' sma_normalized_mse_bayes2metaRL_rnn: {normalized_mse_bayes2metaRL_rnn_bayes_sampled}')
# for untrained_metaRL_rnn
## untrained_metaRL_rnn2bayes_bayes_sampled
source_dim = metaRL_rnn_state_dim
target_dim = bayes_state_dim
dataset = untrained_metaRL_rnn2bayes_dataset_bayes_sampled
untrained_metaRL_rnn2bayes_mapper_bayes_sampled, losses_mse_untrained_metaRL_rnn2bayes_bayes_sampled = train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
untrained_metaRL_rnn2bayes_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_untrained_metaRL_rnn2bayes_bayes_sampled_weights.h5')
torch.save(untrained_metaRL_rnn2bayes_mapper_bayes_sampled.state_dict(), untrained_metaRL_rnn2bayes_mapper_bayes_sampled_save_path)
bayes_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_bayes_states_train_bayes_sampled), axis=1))
mse_untrained_metaRL_rnn2bayes_bayes_sampled = losses_mse_untrained_metaRL_rnn2bayes_bayes_sampled[-1]
normalized_mse_untrained_metaRL_rnn2bayes_bayes_sampled = mse_untrained_metaRL_rnn2bayes_bayes_sampled/ bayes_avg_var_bayes_sampled
print(f'sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
print(f'sma_mse_untrained_metaRL_rnn2bayes: {mse_untrained_metaRL_rnn2bayes_bayes_sampled}')
print(f'sma_normalized_mse_untrained_metaRL_rnn2bayes: {normalized_mse_untrained_metaRL_rnn2bayes_bayes_sampled}')
lines.append('state mapper training: untrained_metaRL_rnn')
lines.append(f' sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
lines.append(f' sma_mse_untrained_metaRL_rnn2bayes: {mse_untrained_metaRL_rnn2bayes_bayes_sampled}')
lines.append(f' sma_normalized_mse_untrained_metaRL_rnn2bayes: {normalized_mse_untrained_metaRL_rnn2bayes_bayes_sampled}')
## bayes2untrained_metaRL_rnn
source_dim = bayes_state_dim
target_dim = metaRL_rnn_state_dim
dataset = bayes2untrained_metaRL_rnn_dataset_bayes_sampled
bayes2untrained_metaRL_rnn_mapper_bayes_sampled, losses_mse_bayes2untrained_metaRL_rnn_bayes_sampled = train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
bayes2untrained_metaRL_rnn_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_bayes2untrained_metaRL_rnn_bayes_sampled_weights.h5')
torch.save(bayes2untrained_metaRL_rnn_mapper_bayes_sampled.state_dict(), bayes2untrained_metaRL_rnn_mapper_bayes_sampled_save_path)
untrained_metaRL_rnn_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_untrained_metaRL_rnn_states_train_bayes_sampled), axis=1))
mse_bayes2untrained_metaRL_rnn_bayes_sampled = losses_mse_bayes2untrained_metaRL_rnn_bayes_sampled[-1]
normalized_mse_bayes2untrained_metaRL_rnn_bayes_sampled = mse_bayes2untrained_metaRL_rnn_bayes_sampled/ untrained_metaRL_rnn_avg_var_bayes_sampled
print(f'sma_untrained_metaRL_rnn_avg_var: {untrained_metaRL_rnn_avg_var_bayes_sampled}')
print(f'sma_mse_bayes2untrained_metaRL_rnn: {mse_bayes2untrained_metaRL_rnn_bayes_sampled}')
print(f'sma_normalized_mse_bayes2untrained_metaRL_rnn: {normalized_mse_bayes2untrained_metaRL_rnn_bayes_sampled}')
lines.append(f' sma_untrained_metaRL_rnn_avg_var: {untrained_metaRL_rnn_avg_var_bayes_sampled}')
lines.append(f' sma_mse_bayes2untrained_metaRL_rnn: {mse_bayes2untrained_metaRL_rnn_bayes_sampled}')
lines.append(f' sma_normalized_mse_bayes2untrained_metaRL_rnn: {normalized_mse_bayes2untrained_metaRL_rnn_bayes_sampled}')
if args.exp_label == 'rl2':
if len(args.layers_after_rnn) > 0: # if bottleneck layer exists
# for trained_metaRL_bottleneck
## metaRL_bottleneck2bayes_bayes_sampled
source_dim = metaRL_bottleneck_state_dim
target_dim = bayes_state_dim
dataset = metaRL_bottleneck2bayes_dataset_bayes_sampled
metaRL_bottleneck2bayes_mapper_bayes_sampled, losses_mse_metaRL_bottleneck2bayes_bayes_sampled = \
train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
metaRL_bottleneck2bayes_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_metaRL_bottleneck2bayes_bayes_sampled_weights.h5')
torch.save(metaRL_bottleneck2bayes_mapper_bayes_sampled.state_dict(), metaRL_bottleneck2bayes_mapper_bayes_sampled_save_path)
bayes_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_bayes_states_train_bayes_sampled), axis=1))
mse_metaRL_bottleneck2bayes_bayes_sampled = losses_mse_metaRL_bottleneck2bayes_bayes_sampled[-1]
normalized_mse_metaRL_bottleneck2bayes_bayes_sampled = mse_metaRL_bottleneck2bayes_bayes_sampled/ bayes_avg_var_bayes_sampled
print(f'sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
print(f'sma_mse_metaRL_bottleneck2bayes: {mse_metaRL_bottleneck2bayes_bayes_sampled}')
print(f'sma_normalized_mse_metaRL_bottleneck2bayes: {normalized_mse_metaRL_bottleneck2bayes_bayes_sampled}')
lines.append('state mapper training: trained_metaRL_bottleneck')
lines.append(f' sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
lines.append(f' sma_mse_metaRL_bottleneck2bayes: {mse_metaRL_bottleneck2bayes_bayes_sampled}')
lines.append(f' sma_normalized_mse_metaRL_bottleneck2bayes: {normalized_mse_metaRL_bottleneck2bayes_bayes_sampled}')
## bayes2metaRL_bottleneck_bayes_sampled
source_dim = bayes_state_dim
target_dim = metaRL_bottleneck_state_dim
dataset = bayes2metaRL_bottleneck_dataset_bayes_sampled
bayes2metaRL_bottleneck_mapper_bayes_sampled, losses_mse_bayes2metaRL_bottleneck_bayes_sampled = \
train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
bayes2metaRL_bottleneck_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_bayes2metaRL_bottleneck_bayes_sampled_weights.h5')
torch.save(bayes2metaRL_bottleneck_mapper_bayes_sampled.state_dict(), bayes2metaRL_bottleneck_mapper_bayes_sampled_save_path)
metaRL_bottleneck_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_metaRL_bottleneck_states_train_bayes_sampled), axis=1))
mse_bayes2metaRL_bottleneck_bayes_sampled = losses_mse_bayes2metaRL_bottleneck_bayes_sampled[-1]
normalized_mse_bayes2metaRL_bottleneck_bayes_sampled = mse_bayes2metaRL_bottleneck_bayes_sampled/ metaRL_bottleneck_avg_var_bayes_sampled
print(f'sma_metaRL_bottleneck_avg_var: {metaRL_bottleneck_avg_var_bayes_sampled}')
print(f'sma_mse_bayes2metaRL_bottleneck: {mse_bayes2metaRL_bottleneck_bayes_sampled}')
print(f'sma_normalized_mse_bayes2metaRL_bottleneck: {normalized_mse_bayes2metaRL_bottleneck_bayes_sampled}')
lines.append(f' sma_metaRL_bottleneck_avg_var: {metaRL_bottleneck_avg_var_bayes_sampled}')
lines.append(f' sma_mse_bayes2metaRL_bottleneck: {mse_bayes2metaRL_bottleneck_bayes_sampled}')
lines.append(f' sma_normalized_mse_bayes2metaRL_bottleneck: {normalized_mse_bayes2metaRL_bottleneck_bayes_sampled}')
# for untrained_metaRL_bottleneck
## untrained_metaRL_bottleneck2bayes_bayes_sampled
source_dim = metaRL_bottleneck_state_dim
target_dim = bayes_state_dim
dataset = untrained_metaRL_bottleneck2bayes_dataset_bayes_sampled
untrained_metaRL_bottleneck2bayes_mapper_bayes_sampled, losses_mse_untrained_metaRL_bottleneck2bayes_bayes_sampled = train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
untrained_metaRL_bottleneck2bayes_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_untrained_metaRL_bottleneck2bayes_bayes_sampled_weights.h5')
torch.save(untrained_metaRL_bottleneck2bayes_mapper_bayes_sampled.state_dict(), untrained_metaRL_bottleneck2bayes_mapper_bayes_sampled_save_path)
bayes_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_bayes_states_train_bayes_sampled), axis=1))
mse_untrained_metaRL_bottleneck2bayes_bayes_sampled = losses_mse_untrained_metaRL_bottleneck2bayes_bayes_sampled[-1]
normalized_mse_untrained_metaRL_bottleneck2bayes_bayes_sampled = mse_untrained_metaRL_bottleneck2bayes_bayes_sampled/ bayes_avg_var_bayes_sampled
print(f'sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
print(f'sma_mse_untrained_metaRL_bottleneck2bayes: {mse_untrained_metaRL_bottleneck2bayes_bayes_sampled}')
print(f'sma_normalized_mse_untrained_metaRL_bottleneck2bayes: {normalized_mse_untrained_metaRL_bottleneck2bayes_bayes_sampled}')
lines.append('state mapper training: untrained_metaRL_bottleneck')
lines.append(f' sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
lines.append(f' sma_mse_untrained_metaRL_bottleneck2bayes: {mse_untrained_metaRL_bottleneck2bayes_bayes_sampled}')
lines.append(f' sma_normalized_mse_untrained_metaRL_bottleneck2bayes: {normalized_mse_untrained_metaRL_bottleneck2bayes_bayes_sampled}')
## bayes2untrained_metaRL_bottleneck
source_dim = bayes_state_dim
target_dim = metaRL_bottleneck_state_dim
dataset = bayes2untrained_metaRL_bottleneck_dataset_bayes_sampled
bayes2untrained_metaRL_bottleneck_mapper_bayes_sampled, losses_mse_bayes2untrained_metaRL_bottleneck_bayes_sampled = train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
bayes2untrained_metaRL_bottleneck_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_bayes2untrained_metaRL_bottleneck_bayes_sampled_weights.h5')
torch.save(bayes2untrained_metaRL_bottleneck_mapper_bayes_sampled.state_dict(), bayes2untrained_metaRL_bottleneck_mapper_bayes_sampled_save_path)
untrained_metaRL_bottleneck_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_untrained_metaRL_bottleneck_states_train_bayes_sampled), axis=1))
mse_bayes2untrained_metaRL_bottleneck_bayes_sampled = losses_mse_bayes2untrained_metaRL_bottleneck_bayes_sampled[-1]
normalized_mse_bayes2untrained_metaRL_bottleneck_bayes_sampled = mse_bayes2untrained_metaRL_bottleneck_bayes_sampled/ untrained_metaRL_bottleneck_avg_var_bayes_sampled
print(f'sma_untrained_metaRL_bottleneck_avg_var: {untrained_metaRL_bottleneck_avg_var_bayes_sampled}')
print(f'sma_mse_bayes2untrained_metaRL_bottleneck: {mse_bayes2untrained_metaRL_bottleneck_bayes_sampled}')
print(f'sma_normalized_mse_bayes2untrained_metaRL_bottleneck: {normalized_mse_bayes2untrained_metaRL_bottleneck_bayes_sampled}')
lines.append(f' sma_untrained_metaRL_bottleneck_avg_var: {untrained_metaRL_bottleneck_avg_var_bayes_sampled}')
lines.append(f' sma_mse_bayes2untrained_metaRL_bottleneck: {mse_bayes2untrained_metaRL_bottleneck_bayes_sampled}')
lines.append(f' sma_normalized_mse_bayes2untrained_metaRL_bottleneck: {normalized_mse_bayes2untrained_metaRL_bottleneck_bayes_sampled}')
if args.exp_label == 'mpc':
# for trained_metaRL_belief
## metaRL_belief2bayes_bayes_sampled
source_dim = metaRL_belief_state_dim
target_dim = bayes_state_dim
dataset = metaRL_belief2bayes_dataset_bayes_sampled
metaRL_belief2bayes_mapper_bayes_sampled, losses_mse_metaRL_belief2bayes_bayes_sampled = \
train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
metaRL_belief2bayes_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_metaRL_belief2bayes_bayes_sampled_weights.h5')
torch.save(metaRL_belief2bayes_mapper_bayes_sampled.state_dict(), metaRL_belief2bayes_mapper_bayes_sampled_save_path)
bayes_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_bayes_states_train_bayes_sampled), axis=1))
mse_metaRL_belief2bayes_bayes_sampled = losses_mse_metaRL_belief2bayes_bayes_sampled[-1]
normalized_mse_metaRL_belief2bayes_bayes_sampled = mse_metaRL_belief2bayes_bayes_sampled/ bayes_avg_var_bayes_sampled
print(f'sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
print(f'sma_mse_metaRL_belief2bayes: {mse_metaRL_belief2bayes_bayes_sampled}')
print(f'sma_normalized_mse_metaRL_belief2bayes: {normalized_mse_metaRL_belief2bayes_bayes_sampled}')
lines.append('state mapper training: trained_metaRL_belief')
lines.append(f' sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
lines.append(f' sma_mse_metaRL_belief2bayes: {mse_metaRL_belief2bayes_bayes_sampled}')
lines.append(f' sma_normalized_mse_metaRL_belief2bayes: {normalized_mse_metaRL_belief2bayes_bayes_sampled}')
## bayes2metaRL_belief_bayes_sampled
source_dim = bayes_state_dim
target_dim = metaRL_belief_state_dim
dataset = bayes2metaRL_belief_dataset_bayes_sampled
bayes2metaRL_belief_mapper_bayes_sampled, losses_mse_bayes2metaRL_belief_bayes_sampled = \
train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
bayes2metaRL_belief_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_bayes2metaRL_belief_bayes_sampled_weights.h5')
torch.save(bayes2metaRL_belief_mapper_bayes_sampled.state_dict(), bayes2metaRL_belief_mapper_bayes_sampled_save_path)
metaRL_belief_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_metaRL_belief_states_train_bayes_sampled), axis=1))
mse_bayes2metaRL_belief_bayes_sampled = losses_mse_bayes2metaRL_belief_bayes_sampled[-1]
normalized_mse_bayes2metaRL_belief_bayes_sampled = mse_bayes2metaRL_belief_bayes_sampled/ metaRL_belief_avg_var_bayes_sampled
print(f'sma_metaRL_belief_avg_var: {metaRL_belief_avg_var_bayes_sampled}')
print(f'sma_mse_bayes2metaRL_belief: {mse_bayes2metaRL_belief_bayes_sampled}')
print(f'sma_normalized_mse_bayes2metaRL_belief: {normalized_mse_bayes2metaRL_belief_bayes_sampled}')
lines.append(f' sma_metaRL_belief_avg_var: {metaRL_belief_avg_var_bayes_sampled}')
lines.append(f' sma_mse_bayes2metaRL_belief: {mse_bayes2metaRL_belief_bayes_sampled}')
lines.append(f' sma_normalized_mse_bayes2metaRL_belief: {normalized_mse_bayes2metaRL_belief_bayes_sampled}')
# for untrained_metaRL_belief
## untrained_metaRL_belief2bayes_bayes_sampled
source_dim = metaRL_belief_state_dim
target_dim = bayes_state_dim
dataset = untrained_metaRL_belief2bayes_dataset_bayes_sampled
untrained_metaRL_belief2bayes_mapper_bayes_sampled, losses_mse_untrained_metaRL_belief2bayes_bayes_sampled = train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
untrained_metaRL_belief2bayes_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_untrained_metaRL_belief2bayes_bayes_sampled_weights.h5')
torch.save(untrained_metaRL_belief2bayes_mapper_bayes_sampled.state_dict(), untrained_metaRL_belief2bayes_mapper_bayes_sampled_save_path)
bayes_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_bayes_states_train_bayes_sampled), axis=1))
mse_untrained_metaRL_belief2bayes_bayes_sampled = losses_mse_untrained_metaRL_belief2bayes_bayes_sampled[-1]
normalized_mse_untrained_metaRL_belief2bayes_bayes_sampled = mse_untrained_metaRL_belief2bayes_bayes_sampled/ bayes_avg_var_bayes_sampled
print(f'sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
print(f'sma_mse_untrained_metaRL_belief2bayes: {mse_untrained_metaRL_belief2bayes_bayes_sampled}')
print(f'sma_normalized_mse_untrained_metaRL_belief2bayes: {normalized_mse_untrained_metaRL_belief2bayes_bayes_sampled}')
lines.append('state mapper training: untrained_metaRL_belief')
lines.append(f' sma_bayes_avg_var: {bayes_avg_var_bayes_sampled}')
lines.append(f' sma_mse_untrained_metaRL_belief2bayes: {mse_untrained_metaRL_belief2bayes_bayes_sampled}')
lines.append(f' sma_normalized_mse_untrained_metaRL_belief2bayes: {normalized_mse_untrained_metaRL_belief2bayes_bayes_sampled}')
## bayes2untrained_metaRL_belief
source_dim = bayes_state_dim
target_dim = metaRL_belief_state_dim
dataset = bayes2untrained_metaRL_belief_dataset_bayes_sampled
bayes2untrained_metaRL_belief_mapper_bayes_sampled, losses_mse_bayes2untrained_metaRL_belief_bayes_sampled = train_state_space_mapper(
source_dim,
target_dim,
dataset,
sma_num_training_epochs,
batch_size=sma_training_batch_size,
validation_split=sma_validation_split,
patience=sma_patience,
min_delta=sma_min_delta,
min_training_epochs=sma_min_training_epochs
)
bayes2untrained_metaRL_belief_mapper_bayes_sampled_save_path = os.path.join(trained_model_path, 'sma_mapper_bayes2untrained_metaRL_belief_bayes_sampled_weights.h5')
torch.save(bayes2untrained_metaRL_belief_mapper_bayes_sampled.state_dict(), bayes2untrained_metaRL_belief_mapper_bayes_sampled_save_path)
untrained_metaRL_belief_avg_var_bayes_sampled = np.average(np.sum(np.square(flattened_untrained_metaRL_belief_states_train_bayes_sampled), axis=1))
mse_bayes2untrained_metaRL_belief_bayes_sampled = losses_mse_bayes2untrained_metaRL_belief_bayes_sampled[-1]
normalized_mse_bayes2untrained_metaRL_belief_bayes_sampled = mse_bayes2untrained_metaRL_belief_bayes_sampled/ untrained_metaRL_belief_avg_var_bayes_sampled
print(f'sma_untrained_metaRL_belief_avg_var: {untrained_metaRL_belief_avg_var_bayes_sampled}')
print(f'sma_mse_bayes2untrained_metaRL_belief: {mse_bayes2untrained_metaRL_belief_bayes_sampled}')
print(f'sma_normalized_mse_bayes2untrained_metaRL_belief: {normalized_mse_bayes2untrained_metaRL_belief_bayes_sampled}')
lines.append(f' sma_untrained_metaRL_belief_avg_var: {untrained_metaRL_belief_avg_var_bayes_sampled}')
lines.append(f' sma_mse_bayes2untrained_metaRL_belief: {mse_bayes2untrained_metaRL_belief_bayes_sampled}')
lines.append(f' sma_normalized_mse_bayes2untrained_metaRL_belief: {normalized_mse_bayes2untrained_metaRL_belief_bayes_sampled}')
# ------------------------------------------------------------------
# testing state space mapper
# ------------------------------------------------------------------
# generate testing dataset for state mapping
ref_trajectories_bayes_test_bayes_sampled = gen_ref_trajectories_by_bayes(
env_name=sma_env_name,
total_trials=sma_total_trials,
biased_beta_prior=biased_beta_prior,
num_envs=sma_num_testing_envs,
num_bandits=sma_num_bandits,
latent_goal_cart_solver=latent_cart_optimal_agent,
) # 1 extra trial to roll out one final step
conditioned_trajectories_metaRL_test_bayes_sampled = gen_metaRL_trajectories_given_ref(
encoder=encoder,
policy_network=policy_network,
args=args,
ref_actions=ref_trajectories_bayes_test_bayes_sampled['bayes_actions'][:, :-1],
ref_rewards=ref_trajectories_bayes_test_bayes_sampled['bayes_rewards'][:, :-1],
ref_observations=ref_trajectories_bayes_test_bayes_sampled['bayes_observations'][:, :-1]
)
conditioned_trajectories_untrained_metaRL_test_bayes_sampled = gen_metaRL_trajectories_given_ref(
encoder=untrained_encoder,
policy_network=untrained_policy_network,
args=args,
ref_actions=ref_trajectories_bayes_test_bayes_sampled['bayes_actions'][:, :-1],
ref_rewards=ref_trajectories_bayes_test_bayes_sampled['bayes_rewards'][:, :-1],
ref_observations=ref_trajectories_bayes_test_bayes_sampled['bayes_observations'][:, :-1]
)
# save testing dataset
with open(os.path.join(trained_model_path, 'sma_ref_trajectories_bayes_test_bayes_sampled.pickle'), 'wb') as fo:
pickle.dump(ref_trajectories_bayes_test_bayes_sampled, fo)
with open(os.path.join(trained_model_path, 'sma_conditioned_trajectories_metaRL_test_bayes_sampled.pickle'), 'wb') as fo:
pickle.dump(conditioned_trajectories_metaRL_test_bayes_sampled, fo)
with open(os.path.join(trained_model_path, 'sma_conditioned_trajectories_untrained_metaRL_test_bayes_sampled.pickle'), 'wb') as fo:
pickle.dump(conditioned_trajectories_untrained_metaRL_test_bayes_sampled, fo)
# get traj for state space analysis: no flattening needed here
bayes_states_test_bayes_sampled = ref_trajectories_bayes_test_bayes_sampled['bayes_states'][:, :-1, :]
metaRL_rnn_states_test_bayes_sampled = conditioned_trajectories_metaRL_test_bayes_sampled['metaRL_rnn_states']
untrained_metaRL_rnn_states_test_bayes_sampled = conditioned_trajectories_untrained_metaRL_test_bayes_sampled['metaRL_rnn_states']
bayes_actions_test_bayes_sampled = ref_trajectories_bayes_test_bayes_sampled['bayes_actions'][:, :-1]
metaRL_actions_test_bayes_sampled = conditioned_trajectories_metaRL_test_bayes_sampled['metaRL_actions'][:, :]
metaRL_all_action_logits_test_bayes_sampled = conditioned_trajectories_metaRL_test_bayes_sampled['metaRL_all_action_logits']
metaRL_a1_probs_test_bayes_sampled = conditioned_trajectories_metaRL_test_bayes_sampled['metaRL_all_action_probs'][:, :, 1]
untrained_metaRL_actions_test_bayes_sampled = conditioned_trajectories_untrained_metaRL_test_bayes_sampled['metaRL_actions'][:, :]
untrained_metaRL_all_action_logits_test_bayes_sampled = conditioned_trajectories_untrained_metaRL_test_bayes_sampled['metaRL_all_action_logits']
untrained_metaRL_a1_probs_test_bayes_sampled = conditioned_trajectories_untrained_metaRL_test_bayes_sampled['metaRL_all_action_probs'][:, :, 1]
transformed_bayes_states_test_bayes_sampled = pca_model_bayes_states_bayes_sampled.transform(bayes_states_test_bayes_sampled.reshape(-1, bayes_state_dim)).\
reshape(sma_num_testing_envs, sma_total_trials, bayes_state_dim)
transformed_metaRL_rnn_states_test_bayes_sampled = pca_model_metaRL_rnn_states_bayes_sampled.transform(metaRL_rnn_states_test_bayes_sampled.reshape(-1, metaRL_rnn_state_dim)).\
reshape(sma_num_testing_envs, sma_total_trials, metaRL_rnn_state_dim)
transformed_metaRL_rnn_states_test_mean_bayes_sampled = np.average(transformed_metaRL_rnn_states_test_bayes_sampled, axis=(0,1))
transformed_untrained_metaRL_rnn_states_test_bayes_sampled = pca_model_untrained_metaRL_rnn_states_bayes_sampled.transform(untrained_metaRL_rnn_states_test_bayes_sampled.reshape(-1, metaRL_rnn_state_dim)).\
reshape(sma_num_testing_envs, sma_total_trials, metaRL_rnn_state_dim)
transformed_untrained_metaRL_rnn_states_test_mean_bayes_sampled = np.average(transformed_untrained_metaRL_rnn_states_test_bayes_sampled, axis=(0,1))
if args.exp_label == 'rl2':
if len(args.layers_after_rnn) > 0: # if bottleneck layer exists
metaRL_bottleneck_states_test_bayes_sampled = conditioned_trajectories_metaRL_test_bayes_sampled['metaRL_bottleneck_states']
untrained_metaRL_bottleneck_states_test_bayes_sampled = conditioned_trajectories_untrained_metaRL_test_bayes_sampled['metaRL_bottleneck_states']
transformed_metaRL_bottleneck_states_test_bayes_sampled = pca_model_metaRL_bottleneck_states_bayes_sampled.transform(metaRL_bottleneck_states_test_bayes_sampled.reshape(-1, metaRL_bottleneck_state_dim)).\
reshape(sma_num_testing_envs, sma_total_trials, metaRL_bottleneck_state_dim)
transformed_untrained_metaRL_bottleneck_states_test_bayes_sampled = pca_model_untrained_metaRL_bottleneck_states_bayes_sampled.transform(untrained_metaRL_bottleneck_states_test_bayes_sampled.reshape(-1, metaRL_bottleneck_state_dim)).\
reshape(sma_num_testing_envs, sma_total_trials, metaRL_bottleneck_state_dim)
if args.exp_label == 'mpc':
metaRL_belief_states_test_bayes_sampled = conditioned_trajectories_metaRL_test_bayes_sampled['metaRL_belief_states']
metaRL_belief_states_mean_only_test_bayes_sampled = conditioned_trajectories_metaRL_test_bayes_sampled['metaRL_belief_states_mean_only']
untrained_metaRL_belief_states_test_bayes_sampled = conditioned_trajectories_untrained_metaRL_test_bayes_sampled['metaRL_belief_states']
untrained_metaRL_belief_states_mean_only_test_bayes_sampled = conditioned_trajectories_untrained_metaRL_test_bayes_sampled['metaRL_belief_states_mean_only']
transformed_metaRL_belief_states_test_bayes_sampled = pca_model_metaRL_belief_states_bayes_sampled.transform(metaRL_belief_states_test_bayes_sampled.reshape(-1, metaRL_belief_state_dim)).\
reshape(sma_num_testing_envs, sma_total_trials, metaRL_belief_state_dim)
transformed_untrained_metaRL_belief_states_test_bayes_sampled = pca_model_untrained_metaRL_belief_states_bayes_sampled.transform(untrained_metaRL_belief_states_test_bayes_sampled.reshape(-1, metaRL_belief_state_dim)).\
reshape(sma_num_testing_envs, sma_total_trials, metaRL_belief_state_dim)
transformed_metaRL_belief_states_test_mean_bayes_sampled = np.average(transformed_metaRL_belief_states_test_bayes_sampled, axis=(0,1))
transformed_untrained_metaRL_belief_states_test_mean_bayes_sampled = np.average(transformed_untrained_metaRL_belief_states_test_bayes_sampled, axis=(0,1))
# ------------------------------------------------------------------
# get state mapping: for dataset 1, without first PCA'ed
# ------------------------------------------------------------------
# for trained_metaRL_rnn
print('\nState mapping testing: trained_metaRL_rnn')
mapped_bayes_states_test_from_metaRL_rnn_bayes_sampled, mapped_bayes_actions_test_from_metaRL_rnn_bayes_sampled = \
get_mapped_bayes_states_and_actions(
metaRL2bayes_mapper=metaRL_rnn2bayes_mapper_bayes_sampled,
metaRL_states=metaRL_rnn_states_test_bayes_sampled,
env_name=sma_env_name,
total_trials=sma_total_trials,
num_bandits=sma_num_bandits,
is_pcaed=False
)
mapped_metaRL_rnn_states_test_from_bayes_bayes_sampled, mapped_metaRL_rnn_all_action_logits_test_from_bayes_bayes_sampled, \
mapped_metaRL_rnn_actions_test_from_bayes_bayes_sampled = get_mapped_metaRL_states_and_actions(
bayes2metaRL_mapper=bayes2metaRL_rnn_mapper_bayes_sampled,
bayes_states=bayes_states_test_bayes_sampled,
args=args,
encoder=encoder,
policy_network=policy_network,
mapped_metaRL_state_type='rnn',
is_pcaed=False,
pca_model_metaRL_for_dataset=None,
pcaed_metaRL_states_mean=None,
bayes_state_dim=bayes_state_dim,
ref_observations=ref_trajectories_bayes_test_bayes_sampled['bayes_observations'][:, :-1]
)
## dissimilarity analysis
normalized_mse_mapped_bayes_from_metaRL_rnn_bayes_sampled, \
avg_expected_return_diff_mapped_bayes_from_metaRL_rnn_bayes_sampled, \
normalized_mse_mapped_metaRL_rnn_from_bayes_bayes_sampled, \
avg_expected_return_diff_mapped_metaRL_rnn_from_bayes_bayes_sampled = \
dissimilarity_analysis(
true_bayes_states=bayes_states_test_bayes_sampled,
mapped_bayes_states=mapped_bayes_states_test_from_metaRL_rnn_bayes_sampled,
true_metaRL_actions=conditioned_trajectories_metaRL_test_bayes_sampled['metaRL_actions'],
mapped_bayes_actions=mapped_bayes_actions_test_from_metaRL_rnn_bayes_sampled,
true_metaRL_states=metaRL_rnn_states_test_bayes_sampled,
mapped_metaRL_states=mapped_metaRL_rnn_states_test_from_bayes_bayes_sampled,
true_bayes_actions=bayes_actions_test_bayes_sampled,
mapped_metaRL_actions=mapped_metaRL_rnn_actions_test_from_bayes_bayes_sampled,
env_name=sma_env_name,
p_bandits=ref_trajectories_bayes_test_bayes_sampled['p_bandits'],
r_bandits=None,
goal_positions_t=ref_trajectories_bayes_test_bayes_sampled['goal_positions_t'][:, :-1],
positions_t=ref_trajectories_bayes_test_bayes_sampled['bayes_observations'][:, :-1]
)
lines.append('\nState mapping testing: trained_metaRL_rnn')
lines.append('metaRL_rnn to bayes:')
lines.append(f' normalized_mse_mapped_bayes_from_metaRL_rnn_bayes_sampled: {normalized_mse_mapped_bayes_from_metaRL_rnn_bayes_sampled}')
lines.append(f' return_diff (metaRL_rnn - mapped_bayes): {avg_expected_return_diff_mapped_bayes_from_metaRL_rnn_bayes_sampled}')
lines.append('bayes to metaRL_rnn:')
lines.append(f' normalized_mse_mapped_metaRL_rnn_from_bayes_bayes_sampled: {normalized_mse_mapped_metaRL_rnn_from_bayes_bayes_sampled}')
lines.append(f' return_diffs (bayes - mapped_metaRL_rnn): {avg_expected_return_diff_mapped_metaRL_rnn_from_bayes_bayes_sampled}')
results['normalized_mse_mapped_bayes_from_metaRL_rnn_bayes_sampled'] = normalized_mse_mapped_bayes_from_metaRL_rnn_bayes_sampled
results['avg_expected_return_diff_mapped_bayes_from_metaRL_rnn_bayes_sampled'] = avg_expected_return_diff_mapped_bayes_from_metaRL_rnn_bayes_sampled
results['normalized_mse_mapped_metaRL_rnn_from_bayes_bayes_sampled'] = normalized_mse_mapped_metaRL_rnn_from_bayes_bayes_sampled
results['avg_expected_return_diff_mapped_metaRL_rnn_from_bayes_bayes_sampled'] = avg_expected_return_diff_mapped_metaRL_rnn_from_bayes_bayes_sampled
## PCA on testing dataset
mapped_metaRL_rnn_a1_probs_test_from_bayes_bayes_sampled = np.exp(mapped_metaRL_rnn_all_action_logits_test_from_bayes_bayes_sampled).reshape(-1,2)[:, 1]/ (np.exp(mapped_metaRL_rnn_all_action_logits_test_from_bayes_bayes_sampled).reshape(-1,2).sum(axis=1))
fig_sma_pca_test_metaRL_rnn_states_bayes_sampled = plot_PCA_bayes_and_metaRL(
pca_model_bayes=pca_model_bayes_states_bayes_sampled,
pca_model_metaRL=pca_model_metaRL_rnn_states_bayes_sampled,
true_bayes_states=bayes_states_test_bayes_sampled.reshape(-1, bayes_state_dim),
mapped_bayes_states=mapped_bayes_states_test_from_metaRL_rnn_bayes_sampled.reshape(-1, bayes_state_dim),
true_metaRL_states=metaRL_rnn_states_test_bayes_sampled.reshape(-1, metaRL_rnn_state_dim),
mapped_metaRL_states=mapped_metaRL_rnn_states_test_from_bayes_bayes_sampled.reshape(-1, metaRL_rnn_state_dim),
true_bayes_actions=bayes_actions_test_bayes_sampled.reshape(-1,1),
mapped_bayes_actions=mapped_bayes_actions_test_from_metaRL_rnn_bayes_sampled.reshape(-1,1),
env_name=sma_env_name,
true_metaRL_a1_probs=metaRL_a1_probs_test_bayes_sampled.reshape(-1,1),
mapped_metaRL_a1_probs=mapped_metaRL_rnn_a1_probs_test_from_bayes_bayes_sampled.reshape(-1,1),
true_metaRL_all_action_logits=metaRL_all_action_logits_test_bayes_sampled.reshape(-1, sma_num_bandits),
mapped_metaRL_all_action_logits=mapped_metaRL_rnn_all_action_logits_test_from_bayes_bayes_sampled.reshape(-1, sma_num_bandits),
total_trials=sma_total_trials,
num_bandits=sma_num_bandits
)
fig_sma_pca_test_metaRL_rnn_states_bayes_sampled.savefig(os.path.join(trained_model_path, 'fig_sma_pca_test_metaRL_rnn_states_bayes_sampled.png'))
# for untrained_metaRL_rnn
print('\nState mapping testing: untrained_metaRL_rnn')
mapped_bayes_states_test_from_untrained_metaRL_rnn_bayes_sampled, mapped_bayes_actions_test_from_untrained_metaRL_rnn_bayes_sampled = \
get_mapped_bayes_states_and_actions(
metaRL2bayes_mapper=untrained_metaRL_rnn2bayes_mapper_bayes_sampled,
metaRL_states=untrained_metaRL_rnn_states_test_bayes_sampled,
env_name=sma_env_name,
total_trials=sma_total_trials,
num_bandits=sma_num_bandits,
is_pcaed=False,
latent_goal_cart_solver=latent_cart_optimal_agent,
ref_observations=ref_trajectories_bayes_test_bayes_sampled['bayes_observations'][:, :-1]
)
mapped_untrained_metaRL_rnn_states_test_from_bayes_bayes_sampled, mapped_untrained_metaRL_rnn_all_action_logits_test_from_bayes_bayes_sampled, \
mapped_untrained_metaRL_rnn_actions_test_from_bayes_bayes_sampled = get_mapped_metaRL_states_and_actions(
bayes2metaRL_mapper=bayes2untrained_metaRL_rnn_mapper_bayes_sampled,
bayes_states=bayes_states_test_bayes_sampled,
args=args,
encoder=encoder,
policy_network=untrained_policy_network,
mapped_metaRL_state_type='rnn',
is_pcaed=False,
pca_model_metaRL_for_dataset=None,
pcaed_metaRL_states_mean=None,
bayes_state_dim=bayes_state_dim,
ref_observations=ref_trajectories_bayes_test_bayes_sampled['bayes_observations'][:, :-1]
)
## dissimilarity ananlysis
normalized_mse_mapped_bayes_from_untrained_metaRL_rnn_bayes_sampled, \
avg_expected_return_diff_mapped_bayes_from_untrained_metaRL_rnn_bayes_sampled, \
normalized_mse_mapped_untrained_metaRL_rnn_from_bayes_bayes_sampled, \
avg_expected_return_diff_mapped_untrained_metaRL_rnn_from_bayes_bayes_sampled = \
dissimilarity_analysis(
true_bayes_states=bayes_states_test_bayes_sampled,
mapped_bayes_states=mapped_bayes_states_test_from_untrained_metaRL_rnn_bayes_sampled,
true_metaRL_actions=conditioned_trajectories_untrained_metaRL_test_bayes_sampled['metaRL_actions'],
mapped_bayes_actions=mapped_bayes_actions_test_from_untrained_metaRL_rnn_bayes_sampled,
true_metaRL_states=untrained_metaRL_rnn_states_test_bayes_sampled,
mapped_metaRL_states=mapped_untrained_metaRL_rnn_states_test_from_bayes_bayes_sampled,
true_bayes_actions=bayes_actions_test_bayes_sampled,
mapped_metaRL_actions=mapped_untrained_metaRL_rnn_actions_test_from_bayes_bayes_sampled,
env_name=sma_env_name,
p_bandits=ref_trajectories_bayes_test_bayes_sampled['p_bandits'],
r_bandits=None,
goal_positions_t=ref_trajectories_bayes_test_bayes_sampled['goal_positions_t'][:, :-1],
positions_t=ref_trajectories_bayes_test_bayes_sampled['bayes_observations'][:, :-1]
)
lines.append('\nState mapping testing: untrained_metaRL_rnn')
lines.append('untrained_metaRL_rnn to bayes:')
lines.append(f' normalized_mse_mapped_bayes_from_untrained_metaRL_rnnbayes_sampled: {normalized_mse_mapped_bayes_from_untrained_metaRL_rnn_bayes_sampled}')
lines.append(f' return_diff (untrained_metaRL_rnn - mapped_bayes): {avg_expected_return_diff_mapped_bayes_from_untrained_metaRL_rnn_bayes_sampled}')
lines.append('bayes to untrained_metaRL_rnn:')
lines.append(f' normalized_mse_mapped_untrained_metaRL_rnn_from_bayes_bayes_sampled: {normalized_mse_mapped_untrained_metaRL_rnn_from_bayes_bayes_sampled}')
lines.append(f' return_diffs (bayes - untrained_mapped_metaRL_rnn): {avg_expected_return_diff_mapped_untrained_metaRL_rnn_from_bayes_bayes_sampled}')
results['normalized_mse_mapped_bayes_from_untrained_metaRL_rnn_bayes_sampled'] = normalized_mse_mapped_bayes_from_untrained_metaRL_rnn_bayes_sampled
results['avg_expected_return_diff_mapped_bayes_from_untrained_metaRL_rnn_bayes_sampled'] = avg_expected_return_diff_mapped_bayes_from_untrained_metaRL_rnn_bayes_sampled
results['normalized_mse_mapped_untrained_metaRL_rnn_from_bayes_bayes_sampled'] = normalized_mse_mapped_untrained_metaRL_rnn_from_bayes_bayes_sampled
results['avg_expected_return_diff_mapped_untrained_metaRL_rnn_from_bayes_bayes_sampled'] = avg_expected_return_diff_mapped_untrained_metaRL_rnn_from_bayes_bayes_sampled
## PCA on testing dataset