from easydict import EasyDict from ding.entry import serial_pipeline_guided_cost halfcheetah_gcl_default_config = dict( env=dict( env_id='HalfCheetah-v3', norm_obs=dict(use_norm=False, ), norm_reward=dict(use_norm=False, ), collector_env_num=1, evaluator_env_num=8, use_act_scale=True, n_evaluator_episode=8, stop_value=12000, ), reward_model=dict( learning_rate=0.001, input_size=23, batch_size=32, action_shape=6, continuous=True, update_per_collect=20, ), policy=dict( cuda=False, on_policy=False, random_collect_size=0, model=dict( obs_shape=17, action_shape=6, twin_critic=True, actor_head_type='reparameterization', actor_head_hidden_size=256, critic_head_hidden_size=256, ), learn=dict( update_per_collect=1, batch_size=256, learning_rate_q=1e-3, learning_rate_policy=1e-3, learning_rate_alpha=3e-4, ignore_done=True, target_theta=0.005, discount_factor=0.99, alpha=0.2, reparameterization=True, auto_alpha=False, ), collect=dict( demonstration_info_path='path', collector_logit=True, n_sample=256, unroll_len=1, ), command=dict(), eval=dict(), other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), ), ) halfcheetah_gcl_default_config = EasyDict(halfcheetah_gcl_default_config) main_config = halfcheetah_gcl_default_config halfcheetah_gcl_default_create_config = dict( env=dict( type='mujoco', import_names=['dizoo.mujoco.envs.mujoco_env'], ), env_manager=dict(type='base'), policy=dict( type='sac', import_names=['ding.policy.sac'], ), replay_buffer=dict(type='naive', ), reward_model=dict(type='guided_cost'), ) halfcheetah_gcl_default_create_config = EasyDict(halfcheetah_gcl_default_create_config) create_config = halfcheetah_gcl_default_create_config if __name__ == '__main__': serial_pipeline_guided_cost((main_config, create_config), seed=0)