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from easydict import EasyDict |
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collector_env_num = 1 |
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evaluator_env_num = 1 |
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walker2d_onppo_config = dict( |
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exp_name='walker2d_onppo_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=collector_env_num, |
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evaluator_env_num=evaluator_env_num, |
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n_evaluator_episode=10, |
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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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recompute_adv=True, |
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action_space='continuous', |
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model=dict( |
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action_space='continuous', |
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obs_shape=17, |
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action_shape=6, |
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), |
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learn=dict( |
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epoch_per_collect=10, |
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update_per_collect=1, |
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batch_size=320, |
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learning_rate=3e-4, |
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value_weight=0.5, |
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entropy_weight=0.001, |
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clip_ratio=0.2, |
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adv_norm=True, |
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value_norm=True, |
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ignore_done=False, |
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grad_clip_type='clip_norm', |
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grad_clip_value=0.5, |
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), |
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collect=dict( |
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collector_env_num=collector_env_num, |
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n_sample=3200, |
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unroll_len=1, |
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discount_factor=0.99, |
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gae_lambda=0.95, |
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), |
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eval=dict(evaluator=dict(eval_freq=500, )), |
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), |
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) |
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walker2d_onppo_config = EasyDict(walker2d_onppo_config) |
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main_config = walker2d_onppo_config |
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walker2d_onppo_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='base'), |
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policy=dict(type='ppo', ), |
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) |
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walker2d_onppo_create_config = EasyDict(walker2d_onppo_create_config) |
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create_config = walker2d_onppo_create_config |
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if __name__ == "__main__": |
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from ding.entry import serial_pipeline_onpolicy |
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serial_pipeline_onpolicy([main_config, create_config], seed=0) |
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