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| from easydict import EasyDict | |
| pendulum_dqn_config = dict( | |
| exp_name='pendulum_dqn_seed0', | |
| env=dict( | |
| collector_env_num=10, | |
| evaluator_env_num=5, | |
| # (bool) Scale output action into legal range. | |
| act_scale=True, | |
| n_evaluator_episode=5, | |
| stop_value=-250, | |
| continuous=False, | |
| # The path to save the game replay | |
| # replay_path='./pendulum_dqn_seed0/video', | |
| ), | |
| policy=dict( | |
| cuda=False, | |
| load_path='pendulum_dqn_seed0/ckpt/ckpt_best.pth.tar', # necessary for eval | |
| model=dict( | |
| obs_shape=3, | |
| action_shape=11, # mean the action shape is 11, 11 discrete actions | |
| encoder_hidden_size_list=[128, 128, 64], | |
| dueling=True, | |
| ), | |
| nstep=1, | |
| discount_factor=0.97, | |
| learn=dict( | |
| batch_size=64, | |
| learning_rate=0.001, | |
| ), | |
| collect=dict(n_sample=8), | |
| eval=dict(evaluator=dict(eval_freq=40, )), | |
| other=dict( | |
| eps=dict( | |
| type='exp', | |
| start=0.95, | |
| end=0.1, | |
| decay=10000, | |
| ), | |
| replay_buffer=dict(replay_buffer_size=20000, ), | |
| ), | |
| ), | |
| ) | |
| pendulum_dqn_config = EasyDict(pendulum_dqn_config) | |
| main_config = pendulum_dqn_config | |
| pendulum_dqn_create_config = dict( | |
| env=dict( | |
| type='pendulum', | |
| import_names=['dizoo.classic_control.pendulum.envs.pendulum_env'], | |
| ), | |
| env_manager=dict(type='base'), | |
| policy=dict(type='dqn'), | |
| replay_buffer=dict(type='deque', import_names=['ding.data.buffer.deque_buffer_wrapper']), | |
| ) | |
| pendulum_dqn_create_config = EasyDict(pendulum_dqn_create_config) | |
| create_config = pendulum_dqn_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial -c pendulum_dqn_config.py -s 0` | |
| from ding.entry import serial_pipeline | |
| serial_pipeline((main_config, create_config), seed=0) | |