DI-engine/dizoo/evogym/config/walker_ppo_config.py

from easydict import EasyDict

walker_ppo_config = dict(
    exp_name='evogym_walker_ppo_seed0',
    env=dict(
        env_id='Walker-v0',
        robot='speed_bot',
        robot_dir='./dizoo/evogym/envs',
        collector_env_num=1,
        evaluator_env_num=1,
        n_evaluator_episode=1,
        stop_value=10,
        manager=dict(shared_memory=True, ),
        # The path to save the game replay
        # replay_path='./evogym_walker_ppo_seed0/video',
    ),
    policy=dict(
        cuda=True,
        recompute_adv=True,
        # load_path="./evogym_walker_ppo_seed0/ckpt/ckpt_best.pth.tar",
        model=dict(
            obs_shape=58,
            action_shape=10,
            action_space='continuous',
        ),
        action_space='continuous',
        learn=dict(
            epoch_per_collect=10,
            batch_size=256,
            learning_rate=3e-4,
            value_weight=0.5,
            entropy_weight=0.0,
            clip_ratio=0.2,
            adv_norm=True,
            value_norm=True,
        ),
        collect=dict(
            n_sample=2048,
            gae_lambda=0.97,
        ),
        eval=dict(evaluator=dict(eval_freq=5000, )),
    )
)
walker_ppo_config = EasyDict(walker_ppo_config)
main_config = walker_ppo_config

walker_ppo_create_config = dict(
    env=dict(
        type='evogym',
        import_names=['dizoo.evogym.envs.evogym_env'],
    ),
    env_manager=dict(type='subprocess'),
    policy=dict(
        type='ppo',
        import_names=['ding.policy.ppo'],
    ),
    replay_buffer=dict(type='naive', ),
)
walker_ppo_create_config = EasyDict(walker_ppo_create_config)
create_config = walker_ppo_create_config

if __name__ == "__main__":
    # or you can enter `ding -m serial -c evogym_walker_ppo_config.py -s 0 --env-step 1e7`
    from ding.entry import serial_pipeline_onpolicy
    serial_pipeline_onpolicy((main_config, create_config), seed=0)