from easydict import EasyDict

walker2d_d4pg_config = dict(
    exp_name='walker2d_d4pg_seed0',
    env=dict(
        env_id='Walker2d-v3',
        norm_obs=dict(use_norm=False, ),
        norm_reward=dict(use_norm=False, ),
        collector_env_num=4,
        evaluator_env_num=4,
        n_evaluator_episode=8,
        stop_value=7000,
    ),
    policy=dict(
        cuda=True,
        priority=True,
        nstep=5,
        random_collect_size=10000,
        model=dict(
            obs_shape=17,
            action_shape=6,
            actor_head_hidden_size=512,
            critic_head_hidden_size=512,
            action_space='regression',
            critic_head_type='categorical',
            v_min=0,
            v_max=2000,  # [1000, 4000]
            n_atom=51,
        ),
        learn=dict(
            update_per_collect=3,  # [1, 4]
            batch_size=256,
            learning_rate_actor=3e-4,
            learning_rate_critic=3e-4,
            ignore_done=False,
            target_theta=0.005,
            discount_factor=0.99,
            actor_update_freq=1,
            noise=False,
        ),
        collect=dict(
            n_sample=8,
            unroll_len=1,
            noise_sigma=0.2,  # [0.1, 0.2]
        ),
        other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
    )
)
walker2d_d4pg_config = EasyDict(walker2d_d4pg_config)
main_config = walker2d_d4pg_config

walker2d_d4pg_create_config = dict(
    env=dict(
        type='mujoco',
        import_names=['dizoo.mujoco.envs.mujoco_env'],
    ),
    env_manager=dict(type='subprocess'),
    policy=dict(
        type='d4pg',
        import_names=['ding.policy.d4pg'],
    ),
)
walker2d_d4pg_create_config = EasyDict(walker2d_d4pg_create_config)
create_config = walker2d_d4pg_create_config

if __name__ == "__main__":
    # or you can enter `ding -m serial -c walker2d_d4pg_config.py -s 0`
    from ding.entry import serial_pipeline
    serial_pipeline([main_config, create_config], seed=0)