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| from easydict import EasyDict | |
| bipedalwalker_ppo_config = dict( | |
| exp_name='bipedalwalker_ppo_seed0', | |
| env=dict( | |
| env_id='BipedalWalker-v3', | |
| collector_env_num=8, | |
| evaluator_env_num=5, | |
| # (bool) Scale output action into legal range. | |
| act_scale=True, | |
| n_evaluator_episode=5, | |
| stop_value=300, | |
| rew_clip=True, | |
| # The path to save the game replay | |
| # replay_path='./bipedalwalker_ppo_seed0/video', | |
| ), | |
| policy=dict( | |
| cuda=False, | |
| load_path="./bipedalwalker_ppo_seed0/ckpt/ckpt_best.pth.tar", | |
| action_space='continuous', | |
| model=dict( | |
| action_space='continuous', | |
| obs_shape=24, | |
| action_shape=4, | |
| ), | |
| learn=dict( | |
| epoch_per_collect=10, | |
| batch_size=64, | |
| learning_rate=0.001, | |
| value_weight=0.5, | |
| entropy_weight=0.01, | |
| clip_ratio=0.2, | |
| adv_norm=True, | |
| ), | |
| collect=dict( | |
| n_sample=2048, | |
| unroll_len=1, | |
| discount_factor=0.99, | |
| gae_lambda=0.95, | |
| ), | |
| ), | |
| ) | |
| bipedalwalker_ppo_config = EasyDict(bipedalwalker_ppo_config) | |
| main_config = bipedalwalker_ppo_config | |
| bipedalwalker_ppo_create_config = dict( | |
| env=dict( | |
| type='bipedalwalker', | |
| import_names=['dizoo.box2d.bipedalwalker.envs.bipedalwalker_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='ppo'), | |
| ) | |
| bipedalwalker_ppo_create_config = EasyDict(bipedalwalker_ppo_create_config) | |
| create_config = bipedalwalker_ppo_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial_onpolicy -c bipedalwalker_ppo_config.py -s 0` | |
| from ding.entry import serial_pipeline_onpolicy | |
| serial_pipeline_onpolicy([main_config, create_config], seed=0) | |