from easydict import EasyDict bipedalwalker_a2c_config = dict( exp_name='bipedalwalker_a2c_seed0', env=dict( env_id='BipedalWalker-v3', collector_env_num=8, evaluator_env_num=8, # (bool) Scale output action into legal range. act_scale=True, n_evaluator_episode=8, stop_value=300, rew_clip=True, # The path to save the game replay # replay_path='./bipedalwalker_a2c_seed0/video', ), policy=dict( cuda=True, # load_path="./bipedalwalker_a2c_seed0/ckpt/ckpt_best.pth.tar", action_space='continuous', model=dict( action_space='continuous', obs_shape=24, action_shape=4, ), learn=dict( # (int) the number of data for a train iteration batch_size=256, learning_rate=0.0003, # (float) loss weight of the value network, the weight of policy network is set to 1 value_weight=0.5, # (float) loss weight of the entropy regularization, the weight of policy network is set to 1 entropy_weight=0.001, # (float) discount factor for future reward, defaults int [0, 1] discount_factor=0.99, adv_norm=True, ), collect=dict( # (int) collect n_sample data, train model n_iteration times n_sample=512, discount_factor=0.99, collector=dict(collect_print_freq=100, ), ), eval=dict(evaluator=dict(eval_freq=100, )), ), ) bipedalwalker_a2c_config = EasyDict(bipedalwalker_a2c_config) main_config = bipedalwalker_a2c_config bipedalwalker_a2c_create_config = dict( env=dict( type='bipedalwalker', import_names=['dizoo.box2d.bipedalwalker.envs.bipedalwalker_env'], ), env_manager=dict(type='subprocess'), policy=dict(type='a2c'), replay_buffer=dict(type='naive'), ) bipedalwalker_a2c_create_config = EasyDict(bipedalwalker_a2c_create_config) create_config = bipedalwalker_a2c_create_config if __name__ == "__main__": # or you can enter `ding -m serial_onpolicy -c bipedalwalker_a2c_config.py -s 0` from ding.entry import serial_pipeline_onpolicy serial_pipeline_onpolicy([main_config, create_config], seed=0)