| from easydict import EasyDict | |
| bigfish_plr_config = dict( | |
| exp_name='bigfish_plr_seed1', | |
| env=dict( | |
| is_train=True, | |
| control_level=False, | |
| env_id='bigfish', | |
| collector_env_num=64, | |
| evaluator_env_num=10, | |
| n_evaluator_episode=50, | |
| stop_value=40, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| model=dict( | |
| obs_shape=[3, 64, 64], | |
| action_shape=15, | |
| encoder_hidden_size_list=[16, 32, 32], | |
| actor_head_hidden_size=256, | |
| critic_head_hidden_size=256, | |
| impala_cnn_encoder=True, | |
| ), | |
| learn=dict( | |
| learning_rate=0.0005, | |
| actor_epoch_per_collect=1, | |
| critic_epoch_per_collect=1, | |
| value_norm=True, | |
| batch_size=16384, | |
| value_weight=0.5, | |
| entropy_weight=0.01, | |
| clip_ratio=0.2, | |
| aux_freq=1, | |
| ), | |
| collect=dict(n_sample=16384, ), | |
| eval=dict(evaluator=dict(eval_freq=96, )), | |
| other=dict(), | |
| ), | |
| level_replay=dict( | |
| strategy='min_margin', | |
| score_transform='rank', | |
| temperature=0.1, | |
| ), | |
| ) | |
| bigfish_plr_config = EasyDict(bigfish_plr_config) | |
| main_config = bigfish_plr_config | |
| bigfish_plr_create_config = dict( | |
| env=dict( | |
| type='procgen', | |
| import_names=['dizoo.procgen.envs.procgen_env'], | |
| ), | |
| env_manager=dict(type='subprocess', ), | |
| policy=dict(type='ppg'), | |
| ) | |
| bigfish_plr_create_config = EasyDict(bigfish_plr_create_config) | |
| create_config = bigfish_plr_create_config | |
| if __name__ == "__main__": | |
| from ding.entry.serial_entry_plr import serial_pipeline_plr | |
| serial_pipeline_plr([main_config, create_config], seed=0) | |