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| from easydict import EasyDict | |
| nstep = 3 | |
| lunarlander_acer_config = dict( | |
| exp_name='lunarlander_acer_seed0', | |
| env=dict( | |
| # Whether to use shared memory. Only effective if "env_manager_type" is 'subprocess' | |
| # Env number respectively for collector and evaluator. | |
| collector_env_num=8, | |
| evaluator_env_num=8, | |
| env_id='LunarLander-v2', | |
| n_evaluator_episode=8, | |
| stop_value=200, | |
| ), | |
| policy=dict( | |
| # Whether to use cuda for network. | |
| cuda=False, | |
| # Model config used for model creating. Remember to change this, | |
| # especially "obs_shape" and "action_shape" according to specific env. | |
| model=dict( | |
| obs_shape=8, | |
| action_shape=4, | |
| encoder_hidden_size_list=[512, 64], | |
| # Whether to use dueling head. | |
| ), | |
| # Reward's future discount facotr, aka. gamma. | |
| discount_factor=0.99, | |
| # How many steps in td error. | |
| nstep=nstep, | |
| unroll_len=32, | |
| # learn_mode config | |
| learn=dict( | |
| # (int) collect n_sample data, train model update_per_collect times | |
| # here we follow impala serial pipeline | |
| update_per_collect=10, | |
| # (int) the number of data for a train iteration | |
| batch_size=32, | |
| # grad_clip_type='clip_norm', | |
| # clip_value=10, | |
| learning_rate_actor=0.0001, | |
| learning_rate_critic=0.0001, | |
| # (float) loss weight of the value network, the weight of policy network is set to 1 | |
| # (float) loss weight of the entropy regularization, the weight of policy network is set to 1 | |
| entropy_weight=0.0, | |
| # (float) discount factor for future reward, defaults int [0, 1] | |
| discount_factor=0.99, | |
| # (float) additional discounting parameter | |
| # (int) the trajectory length to calculate v-trace target | |
| # (float) clip ratio of importance weights | |
| c_clip_ratio=10, | |
| ), | |
| collect=dict( | |
| # (int) collect n_sample data, train model n_iteration times | |
| n_sample=16, | |
| # (float) discount factor for future reward, defaults int [0, 1] | |
| discount_factor=0.99, | |
| gae_lambda=0.95, | |
| collector=dict(collect_print_freq=1000, ), | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=5000, )), | |
| other=dict(replay_buffer=dict(replay_buffer_size=50000, ), ), | |
| ), | |
| ) | |
| lunarlander_acer_config = EasyDict(lunarlander_acer_config) | |
| main_config = lunarlander_acer_config | |
| lunarlander_acer_create_config = dict( | |
| env=dict( | |
| type='lunarlander', | |
| import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='acer'), | |
| replay_buffer=dict(type='naive') | |
| ) | |
| lunarlander_acer_create_config = EasyDict(lunarlander_acer_create_config) | |
| create_config = lunarlander_acer_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial -c lunarlander_acer_config.py -s 0` | |
| from ding.entry import serial_pipeline | |
| serial_pipeline([main_config, create_config], seed=0) | |