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| from easydict import EasyDict | |
| hopper_trex_sac_config = dict( | |
| exp_name='hopper_trex_sac_seed0', | |
| env=dict( | |
| env_id='Hopper-v3', | |
| norm_obs=dict(use_norm=False, ), | |
| norm_reward=dict(use_norm=False, ), | |
| collector_env_num=1, | |
| evaluator_env_num=8, | |
| n_evaluator_episode=8, | |
| stop_value=6000, | |
| ), | |
| reward_model=dict( | |
| learning_rate=1e-5, | |
| min_snippet_length=30, | |
| max_snippet_length=100, | |
| checkpoint_min=1000, | |
| checkpoint_max=9000, | |
| checkpoint_step=1000, | |
| update_per_collect=1, | |
| # Users should add their own model path here. Model path should lead to a model. | |
| # Absolute path is recommended. | |
| # In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``. | |
| # However, here in ``expert_model_path``, it is ``exp_name`` of the expert config. | |
| expert_model_path='model_path_placeholder', | |
| # Path where to store the reward model | |
| reward_model_path='data_path_placeholder + /Hopper.params', | |
| # Users should add their own data path here. Data path should lead to a file to store data or load the stored data. | |
| # Absolute path is recommended. | |
| # In DI-engine, it is usually located in ``exp_name`` directory | |
| # See ding/entry/application_entry_trex_collect_data.py to collect the data | |
| data_path='data_path_placeholder', | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| random_collect_size=10000, | |
| model=dict( | |
| obs_shape=11, | |
| action_shape=3, | |
| twin_critic=True, | |
| action_space='reparameterization', | |
| actor_head_hidden_size=256, | |
| critic_head_hidden_size=256, | |
| ), | |
| learn=dict( | |
| update_per_collect=1, | |
| batch_size=256, | |
| learning_rate_q=1e-3, | |
| learning_rate_policy=1e-3, | |
| learning_rate_alpha=3e-4, | |
| ignore_done=False, | |
| target_theta=0.005, | |
| discount_factor=0.99, | |
| alpha=0.2, | |
| reparameterization=True, | |
| auto_alpha=False, | |
| ), | |
| collect=dict( | |
| n_sample=1, | |
| unroll_len=1, | |
| ), | |
| command=dict(), | |
| eval=dict(), | |
| other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), | |
| ), | |
| ) | |
| hopper_trex_sac_config = EasyDict(hopper_trex_sac_config) | |
| main_config = hopper_trex_sac_config | |
| hopper_trex_sac_create_config = dict( | |
| env=dict( | |
| type='mujoco', | |
| import_names=['dizoo.mujoco.envs.mujoco_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict( | |
| type='sac', | |
| import_names=['ding.policy.sac'], | |
| ), | |
| replay_buffer=dict(type='naive', ), | |
| ) | |
| hopper_trex_sac_create_config = EasyDict(hopper_trex_sac_create_config) | |
| create_config = hopper_trex_sac_create_config | |
| if __name__ == '__main__': | |
| # Users should first run ``hopper_sac_config.py`` to save models (or checkpoints). | |
| # Note: Users should check that the checkpoints generated should include iteration_'checkpoint_min'.pth.tar, iteration_'checkpoint_max'.pth.tar with the interval checkpoint_step | |
| # where checkpoint_max, checkpoint_min, checkpoint_step are specified above. | |
| import argparse | |
| import torch | |
| from ding.entry import trex_collecting_data | |
| from ding.entry import serial_pipeline_trex | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--cfg', type=str, default='please enter abs path for this file') | |
| parser.add_argument('--seed', type=int, default=0) | |
| parser.add_argument('--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu') | |
| args = parser.parse_args() | |
| # The function ``trex_collecting_data`` below is to collect episodic data for training the reward model in trex. | |
| trex_collecting_data(args) | |
| serial_pipeline_trex([main_config, create_config]) | |