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from easydict import EasyDict |
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walker2d_ddpg_config = dict( |
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exp_name='walker2d_ddpg_seed0', |
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env=dict( |
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env_id='Walker2d-v3', |
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norm_obs=dict(use_norm=False, ), |
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norm_reward=dict(use_norm=False, ), |
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collector_env_num=1, |
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evaluator_env_num=8, |
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n_evaluator_episode=8, |
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stop_value=6000, |
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), |
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policy=dict( |
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cuda=True, |
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random_collect_size=25000, |
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model=dict( |
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obs_shape=17, |
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action_shape=6, |
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twin_critic=False, |
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actor_head_hidden_size=256, |
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critic_head_hidden_size=256, |
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action_space='regression', |
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), |
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learn=dict( |
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update_per_collect=1, |
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batch_size=256, |
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learning_rate_actor=1e-3, |
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learning_rate_critic=1e-3, |
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ignore_done=False, |
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target_theta=0.005, |
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discount_factor=0.99, |
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actor_update_freq=1, |
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noise=False, |
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), |
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collect=dict( |
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n_sample=1, |
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unroll_len=1, |
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noise_sigma=0.1, |
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), |
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other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), |
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) |
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) |
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walker2d_ddpg_config = EasyDict(walker2d_ddpg_config) |
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main_config = walker2d_ddpg_config |
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walker2d_ddpg_create_config = dict( |
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env=dict( |
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type='mujoco', |
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import_names=['dizoo.mujoco.envs.mujoco_env'], |
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), |
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env_manager=dict(type='subprocess'), |
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policy=dict( |
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type='ddpg', |
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import_names=['ding.policy.ddpg'], |
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), |
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replay_buffer=dict(type='naive', ), |
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) |
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walker2d_ddpg_create_config = EasyDict(walker2d_ddpg_create_config) |
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create_config = walker2d_ddpg_create_config |
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if __name__ == "__main__": |
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from ding.entry import serial_pipeline |
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serial_pipeline([main_config, create_config], seed=0) |
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