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from easydict import EasyDict |
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obs_shape = 17 |
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act_shape = 6 |
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walker2d_sqil_config = dict( |
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exp_name='walker2d_sqil_sac_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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expert_random_collect_size=10000, |
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model=dict( |
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obs_shape=obs_shape, |
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action_shape=act_shape, |
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twin_critic=True, |
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action_space='reparameterization', |
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actor_head_hidden_size=256, |
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critic_head_hidden_size=256, |
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), |
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nstep=1, |
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discount_factor=0.97, |
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learn=dict( |
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update_per_collect=1, |
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batch_size=64, |
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learning_rate_q=1e-3, |
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learning_rate_policy=1e-3, |
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learning_rate_alpha=3e-4, |
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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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alpha=0.2, |
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reparameterization=True, |
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auto_alpha=True, |
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), |
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collect=dict( |
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n_sample=16, |
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unroll_len=1, |
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model_path='model_path_placeholder', |
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), |
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eval=dict(evaluator=dict(eval_freq=500, )), |
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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_sqil_config = EasyDict(walker2d_sqil_config) |
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main_config = walker2d_sqil_config |
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walker2d_sqil_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(type='sqil_sac', ), |
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replay_buffer=dict(type='naive', ), |
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) |
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walker2d_sqil_create_config = EasyDict(walker2d_sqil_create_config) |
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create_config = walker2d_sqil_create_config |
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if __name__ == "__main__": |
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from ding.entry import serial_pipeline_sqil |
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from dizoo.mujoco.config.walker2d_sac_config import walker2d_sac_config, walker2d_sac_create_config |
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expert_main_config = walker2d_sac_config |
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expert_create_config = walker2d_sac_create_config |
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serial_pipeline_sqil( |
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[main_config, create_config], |
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[expert_main_config, expert_create_config], |
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max_env_step=5000000, |
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seed=0, |
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) |
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