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| from easydict import EasyDict | |
| collector_env_num = 8 | |
| evaluator_env_num = 5 | |
| minigrid_icm_onppo_config = dict( | |
| exp_name='minigrid_AKTDT-7x7_icm_onppo_seed0', | |
| env=dict( | |
| collector_env_num=collector_env_num, | |
| evaluator_env_num=evaluator_env_num, | |
| n_evaluator_episode=evaluator_env_num, | |
| # minigrid env id: 'MiniGrid-Empty-8x8-v0', 'MiniGrid-FourRooms-v0','MiniGrid-DoorKey-16x16-v0','MiniGrid-AKTDT-7x7-1-v0' | |
| env_id='MiniGrid-NoisyTV-v0', | |
| max_step=100, | |
| stop_value=12, # run fixed env_steps for MiniGrid-AKTDT-7x7-1-v0 | |
| # stop_value=0.96, | |
| ), | |
| reward_model=dict( | |
| intrinsic_reward_type='add', | |
| # intrinsic_reward_weight means the relative weight of ICM intrinsic_reward. | |
| # Specifically for sparse reward env MiniGrid, in this env, | |
| # if reach goal, the agent get reward ~1, otherwise 0, | |
| # We could set the intrinsic_reward_weight approximately equal to the inverse of max_episode_steps. | |
| # Please refer to rnd_reward_model for details. | |
| intrinsic_reward_weight=0.003, # 1/300 | |
| learning_rate=3e-4, | |
| obs_shape=2835, # 2715 in MiniGrid-AKTDT-7x7-1-v0 env | |
| batch_size=320, | |
| update_per_collect=50, | |
| clear_buffer_per_iters=int(1e3), | |
| extrinsic_reward_norm=True, | |
| extrinsic_reward_norm_max=1, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| recompute_adv=True, | |
| action_space='discrete', | |
| model=dict( | |
| obs_shape=2835, # 2715 in MiniGrid-AKTDT-7x7-1-v0 env | |
| action_shape=7, | |
| action_space='discrete', | |
| encoder_hidden_size_list=[256, 128, 64, 64], | |
| critic_head_hidden_size=64, | |
| actor_head_hidden_size=64, | |
| ), | |
| learn=dict( | |
| epoch_per_collect=10, | |
| update_per_collect=1, | |
| batch_size=320, | |
| learning_rate=3e-4, | |
| value_weight=0.5, | |
| entropy_weight=0.001, | |
| clip_ratio=0.2, | |
| adv_norm=True, | |
| value_norm=True, | |
| ), | |
| collect=dict( | |
| n_sample=3200, | |
| unroll_len=1, | |
| discount_factor=0.99, | |
| gae_lambda=0.95, | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=1000, )), | |
| ), | |
| ) | |
| minigrid_icm_onppo_config = EasyDict(minigrid_icm_onppo_config) | |
| main_config = minigrid_icm_onppo_config | |
| minigrid_icm_onppo_create_config = dict( | |
| env=dict( | |
| type='minigrid', | |
| import_names=['dizoo.minigrid.envs.minigrid_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='ppo'), | |
| reward_model=dict(type='icm'), | |
| ) | |
| minigrid_icm_onppo_create_config = EasyDict(minigrid_icm_onppo_create_config) | |
| create_config = minigrid_icm_onppo_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial -c minigrid_icm_onppo_config.py -s 0` | |
| from ding.entry import serial_pipeline_reward_model_onpolicy | |
| serial_pipeline_reward_model_onpolicy([main_config, create_config], seed=0, max_env_step=int(10e6)) |