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| from easydict import EasyDict | |
| cartpole_a2c_config = dict( | |
| exp_name='cartpole_a2c_seed0', | |
| env=dict( | |
| collector_env_num=8, | |
| evaluator_env_num=5, | |
| n_evaluator_episode=5, | |
| stop_value=195, | |
| ), | |
| policy=dict( | |
| cuda=False, | |
| # (bool) whether use on-policy training pipeline(behaviour policy and training policy are the same) | |
| model=dict( | |
| obs_shape=4, | |
| action_shape=2, | |
| encoder_hidden_size_list=[128, 128, 64], | |
| ), | |
| learn=dict( | |
| batch_size=40, | |
| learning_rate=0.001, | |
| # (float) loss weight of the entropy regularization, the weight of policy network is set to 1 | |
| entropy_weight=0.01, | |
| ), | |
| collect=dict( | |
| # (int) collect n_sample data, train model n_iteration times | |
| n_sample=80, | |
| # (float) the trade-off factor lambda to balance 1step td and mc | |
| gae_lambda=0.95, | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=50, )), | |
| ), | |
| ) | |
| cartpole_a2c_config = EasyDict(cartpole_a2c_config) | |
| main_config = cartpole_a2c_config | |
| cartpole_a2c_create_config = dict( | |
| env=dict( | |
| type='cartpole', | |
| import_names=['dizoo.classic_control.cartpole.envs.cartpole_env'], | |
| ), | |
| env_manager=dict(type='base'), | |
| policy=dict(type='a2c'), | |
| ) | |
| cartpole_a2c_create_config = EasyDict(cartpole_a2c_create_config) | |
| create_config = cartpole_a2c_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial_onpolicy -c cartpole_a2c_config.py -s 0` | |
| from ding.entry import serial_pipeline_onpolicy | |
| serial_pipeline_onpolicy((main_config, create_config), seed=0) | |