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| from easydict import EasyDict | |
| collector_env_num = 8 | |
| evaluator_env_num = 8 | |
| lunarlander_ppo_rnd_config = dict( | |
| exp_name='lunarlander_rnd_onppo_seed0', | |
| env=dict( | |
| collector_env_num=collector_env_num, | |
| evaluator_env_num=evaluator_env_num, | |
| env_id='LunarLander-v2', | |
| n_evaluator_episode=evaluator_env_num, | |
| stop_value=200, | |
| ), | |
| reward_model=dict( | |
| intrinsic_reward_type='add', | |
| # means the relative weight of RND intrinsic_reward. | |
| # If intrinsic_reward_weight=None, we will automatically set it based on | |
| # the absolute value of the difference between max and min extrinsic reward in the sampled mini-batch | |
| # please refer to rnd_reward_model for details. | |
| intrinsic_reward_weight=None, | |
| # means the rescale value of RND intrinsic_reward only used when intrinsic_reward_weight is None | |
| # please refer to rnd_reward_model for details. | |
| intrinsic_reward_rescale=0.001, | |
| learning_rate=5e-4, | |
| obs_shape=8, | |
| batch_size=320, | |
| update_per_collect=4, | |
| obs_norm=True, | |
| obs_norm_clamp_min=-1, | |
| obs_norm_clamp_max=1, | |
| clear_buffer_per_iters=10, | |
| ), | |
| policy=dict( | |
| recompute_adv=True, | |
| cuda=True, | |
| action_space='discrete', | |
| model=dict( | |
| obs_shape=8, | |
| action_shape=4, | |
| action_space='discrete', | |
| ), | |
| learn=dict( | |
| epoch_per_collect=10, | |
| update_per_collect=1, | |
| batch_size=64, | |
| learning_rate=3e-4, | |
| value_weight=0.5, | |
| entropy_weight=0.01, | |
| clip_ratio=0.2, | |
| adv_norm=True, | |
| value_norm=True, | |
| ), | |
| collect=dict( | |
| n_sample=512, | |
| collector_env_num=collector_env_num, | |
| unroll_len=1, | |
| discount_factor=0.99, | |
| gae_lambda=0.95, | |
| ), | |
| ), | |
| ) | |
| lunarlander_ppo_rnd_config = EasyDict(lunarlander_ppo_rnd_config) | |
| main_config = lunarlander_ppo_rnd_config | |
| lunarlander_ppo_rnd_create_config = dict( | |
| env=dict( | |
| type='lunarlander', | |
| import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='ppo'), | |
| reward_model=dict(type='rnd') | |
| ) | |
| lunarlander_ppo_rnd_create_config = EasyDict(lunarlander_ppo_rnd_create_config) | |
| create_config = lunarlander_ppo_rnd_create_config | |
| if __name__ == "__main__": | |
| from ding.entry import serial_pipeline_reward_model_onpolicy | |
| serial_pipeline_reward_model_onpolicy([main_config, create_config], seed=0) | |