| from easydict import EasyDict | |
| maze_size = 16 | |
| num_actions = 4 | |
| maze_pc_config = dict( | |
| exp_name="maze_pc_seed0", | |
| train_seeds=5, | |
| env=dict( | |
| collector_env_num=8, | |
| evaluator_env_num=5, | |
| n_evaluator_episode=5, | |
| env_id='Maze', | |
| size=maze_size, | |
| wall_type='tunnel', | |
| stop_value=1, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| maze_size=maze_size, | |
| num_actions=num_actions, | |
| max_bfs_steps=100, | |
| model=dict( | |
| obs_shape=[8, maze_size, maze_size], | |
| action_shape=num_actions, | |
| encoder_hidden_size_list=[ | |
| 128, | |
| 256, | |
| 512, | |
| 1024, | |
| ], | |
| ), | |
| learn=dict( | |
| batch_size=32, | |
| learning_rate=0.0005, | |
| train_epoch=100, | |
| optimizer='Adam', | |
| ), | |
| eval=dict(evaluator=dict(n_episode=5)), | |
| collect=dict(), | |
| ), | |
| ) | |
| maze_pc_config = EasyDict(maze_pc_config) | |
| main_config = maze_pc_config | |
| maze_pc_create_config = dict( | |
| env=dict( | |
| type='maze', | |
| import_names=['dizoo.maze.envs.maze_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='pc_bfs'), | |
| ) | |
| maze_pc_create_config = EasyDict(maze_pc_create_config) | |
| create_config = maze_pc_create_config | |
| if __name__ == '__main__': | |
| from ding.entry import serial_pipeline_pc | |
| serial_pipeline_pc([maze_pc_config, maze_pc_create_config], seed=0) | |