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Add torchrl tensordict dataset and replay buffer.
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206
grl_pipelines/benchmark/gmpg/vpsde/antmaze_large_diverse.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,206 @@ | ||
| import torch | ||
| from easydict import EasyDict | ||
|
|
||
| env_id = "antmaze-large-diverse-v0" | ||
| action_size = 8 | ||
| state_size = 29 | ||
| algorithm_type = "GMPG" | ||
| solver_type = "ODESolver" | ||
| model_type = "DiffusionModel" | ||
| generative_model_type = "VPSDE" | ||
| path = dict( | ||
| type="linear_vp_sde", | ||
| beta_0=0.1, | ||
| beta_1=20.0, | ||
| ) | ||
| model_loss_type = "flow_matching" | ||
| project_name = f"d4rl-{env_id}-{algorithm_type}-{generative_model_type}" | ||
| device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") | ||
| t_embedding_dim = 32 | ||
| t_encoder = dict( | ||
| type="GaussianFourierProjectionTimeEncoder", | ||
| args=dict( | ||
| embed_dim=t_embedding_dim, | ||
| scale=30.0, | ||
| ), | ||
| ) | ||
| model = dict( | ||
| device=device, | ||
| x_size=action_size, | ||
| solver=dict( | ||
| type="ODESolver", | ||
| args=dict( | ||
| library="torchdiffeq_adjoint", | ||
| ), | ||
| ), | ||
| path=path, | ||
| reverse_path=path, | ||
| model=dict( | ||
| type="velocity_function", | ||
| args=dict( | ||
| t_encoder=t_encoder, | ||
| backbone=dict( | ||
| type="TemporalSpatialResidualNet", | ||
| args=dict( | ||
| hidden_sizes=[512, 256, 128], | ||
| output_dim=action_size, | ||
| t_dim=t_embedding_dim, | ||
| condition_dim=state_size, | ||
| condition_hidden_dim=32, | ||
| t_condition_hidden_dim=128, | ||
| ), | ||
| ), | ||
| ), | ||
| ), | ||
| ) | ||
|
|
||
| config = EasyDict( | ||
| train=dict( | ||
| project=project_name, | ||
| device=device, | ||
| wandb=dict(project=f"IQL-{env_id}-{algorithm_type}-{generative_model_type}"), | ||
| simulator=dict( | ||
| type="GymEnvSimulator", | ||
| args=dict( | ||
| env_id=env_id, | ||
| ), | ||
| ), | ||
| dataset=dict( | ||
| type="GPOD4RLDataset", | ||
| args=dict( | ||
| env_id=env_id, | ||
| device=device, | ||
| ), | ||
| ), | ||
| model=dict( | ||
| GPPolicy=dict( | ||
| device=device, | ||
| model_type=model_type, | ||
| model_loss_type=model_loss_type, | ||
| model=model, | ||
| critic=dict( | ||
| device=device, | ||
| q_alpha=1.0, | ||
| DoubleQNetwork=dict( | ||
| backbone=dict( | ||
| type="ConcatenateMLP", | ||
| args=dict( | ||
| hidden_sizes=[action_size + state_size, 256, 256], | ||
| output_size=1, | ||
| activation="relu", | ||
| ), | ||
| ), | ||
| ), | ||
| VNetwork=dict( | ||
| backbone=dict( | ||
| type="MultiLayerPerceptron", | ||
| args=dict( | ||
| hidden_sizes=[state_size, 256, 256], | ||
| output_size=1, | ||
| activation="relu", | ||
| ), | ||
| ), | ||
| ), | ||
| ), | ||
| ), | ||
| GuidedPolicy=dict( | ||
| model_type=model_type, | ||
| model=model, | ||
| ), | ||
| ), | ||
| parameter=dict( | ||
| algorithm_type=algorithm_type, | ||
| behaviour_policy=dict( | ||
| batch_size=4096, | ||
| learning_rate=1e-4, | ||
| epochs=4000, | ||
| ), | ||
| t_span=32, | ||
| critic=dict( | ||
| batch_size=4096, | ||
| epochs=4000, | ||
| learning_rate=1e-4, | ||
| discount_factor=0.99, | ||
| update_momentum=0.005, | ||
| tau=0.9, | ||
| method="iql", | ||
| ), | ||
| guided_policy=dict( | ||
| batch_size=40960, | ||
| epochs=100, | ||
| learning_rate=5e-6, | ||
| copy_from_basemodel=True, | ||
| gradtime_step=1000, | ||
| eta=0.5, | ||
| ), | ||
| evaluation=dict( | ||
| eval=True, | ||
| repeat=10, | ||
| interval=5, | ||
| ), | ||
| checkpoint_path=f"./{project_name}/checkpoint", | ||
| checkpoint_freq=5, | ||
| ), | ||
| ), | ||
| deploy=dict( | ||
| device=device, | ||
| env=dict( | ||
| env_id=env_id, | ||
| seed=0, | ||
| ), | ||
| t_span=32, | ||
| ), | ||
| ) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
|
|
||
| import gym | ||
| import d4rl | ||
| import numpy as np | ||
|
|
||
| from grl.algorithms.gmpg import GPAlgorithm | ||
| from grl.utils.log import log | ||
|
|
||
| def gp_pipeline(config): | ||
|
|
||
| gp = GPAlgorithm(config) | ||
|
|
||
| # --------------------------------------- | ||
| # Customized train code ↓ | ||
| # --------------------------------------- | ||
| gp.train() | ||
| # --------------------------------------- | ||
| # Customized train code ↑ | ||
| # --------------------------------------- | ||
|
|
||
| # --------------------------------------- | ||
| # Customized deploy code ↓ | ||
| # --------------------------------------- | ||
|
|
||
| agent = gp.deploy() | ||
| env = gym.make(config.deploy.env.env_id) | ||
| total_reward_list = [] | ||
| for i in range(100): | ||
| observation = env.reset() | ||
| total_reward = 0 | ||
| while True: | ||
| # env.render() | ||
| observation, reward, done, _ = env.step(agent.act(observation)) | ||
| total_reward += reward | ||
| if done: | ||
| observation = env.reset() | ||
| print(f"Episode {i}, total_reward: {total_reward}") | ||
| total_reward_list.append(total_reward) | ||
| break | ||
|
|
||
| print( | ||
| f"Average total reward: {np.mean(total_reward_list)}, std: {np.std(total_reward_list)}" | ||
| ) | ||
|
|
||
| # --------------------------------------- | ||
| # Customized deploy code ↑ | ||
| # --------------------------------------- | ||
|
|
||
| log.info("config: \n{}".format(config)) | ||
| gp_pipeline(config) |
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