from easydict import EasyDict pendulum_td3_generation_config = dict( exp_name='pendulum_td3_generation', env=dict( collector_env_num=8, evaluator_env_num=10, # (bool) Scale output action into legal range. act_scale=True, n_evaluator_episode=10, stop_value=-250, ), policy=dict( cuda=False, priority=False, random_collect_size=800, model=dict( obs_shape=3, action_shape=1, twin_critic=True, action_space='regression', ), learn=dict( update_per_collect=2, batch_size=128, learning_rate_actor=0.001, learning_rate_critic=0.001, ignore_done=True, actor_update_freq=2, noise=True, noise_sigma=0.2, noise_range=dict( min=-0.5, max=0.5, ), learner=dict( load_path='./td3/ckpt/ckpt_best.pth.tar', hook=dict( load_ckpt_before_run='./td3/ckpt/ckpt_best.pth.tar', save_ckpt_after_run=False, ) ), ), collect=dict( n_sample=10, noise_sigma=0.1, collector=dict(collect_print_freq=1000, ), save_path='expert.pkl', data_type='hdf5', ), eval=dict(evaluator=dict(eval_freq=100, ), ), other=dict(replay_buffer=dict( replay_buffer_size=40000, ), ), ), ) pendulum_td3_generation_config = EasyDict(pendulum_td3_generation_config) main_config = pendulum_td3_generation_config pendulum_td3_generation_create_config = dict( env=dict( type='pendulum', import_names=['dizoo.classic_control.pendulum.envs.pendulum_env'], ), env_manager=dict(type='base'), policy=dict(type='ddpg'), ) pendulum_td3_generation_create_config = EasyDict(pendulum_td3_generation_create_config) create_config = pendulum_td3_generation_create_config