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from easydict import EasyDict |
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main_config = dict( |
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exp_name="antmaze_umaze_pd_seed0", |
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env=dict( |
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env_id='antmaze-umaze-v0', |
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collector_env_num=1, |
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evaluator_env_num=8, |
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use_act_scale=True, |
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n_evaluator_episode=8, |
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returns_scale=1.0, |
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termination_penalty=-100, |
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max_path_length=1000, |
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use_padding=True, |
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include_returns=True, |
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normed=False, |
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stop_value=8000, |
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horizon=256, |
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obs_dim=29, |
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action_dim=8, |
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), |
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policy=dict( |
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cuda=True, |
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model=dict( |
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diffuser_model='GaussianDiffusion', |
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diffuser_model_cfg=dict( |
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model='DiffusionUNet1d', |
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model_cfg=dict( |
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transition_dim=37, |
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dim=32, |
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dim_mults=[1, 2, 4, 8], |
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returns_condition=False, |
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kernel_size=5, |
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attention=False, |
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), |
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horizon=256, |
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obs_dim=29, |
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action_dim=8, |
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n_timesteps=20, |
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predict_epsilon=False, |
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loss_discount=1, |
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action_weight=10, |
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), |
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value_model='ValueDiffusion', |
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value_model_cfg=dict( |
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model='TemporalValue', |
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model_cfg=dict( |
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horizon = 256, |
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transition_dim=37, |
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dim=32, |
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dim_mults=[1, 2, 4, 8], |
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kernel_size=5, |
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), |
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horizon=256, |
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obs_dim=29, |
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action_dim=8, |
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n_timesteps=20, |
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predict_epsilon=True, |
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loss_discount=1, |
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), |
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n_guide_steps=2, |
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scale=0.1, |
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t_stopgrad=2, |
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scale_grad_by_std=True, |
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), |
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normalizer='GaussianNormalizer', |
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learn=dict( |
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data_path=None, |
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train_epoch=60000, |
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gradient_accumulate_every=2, |
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batch_size=32, |
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learning_rate=2e-4, |
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discount_factor=0.99, |
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plan_batch_size=64, |
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learner=dict(hook=dict(save_ckpt_after_iter=1000000000, )), |
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), |
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collect=dict(data_type='diffuser_traj', ), |
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eval=dict( |
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evaluator=dict(eval_freq=500, ), |
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test_ret=0.9, |
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), |
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other=dict(replay_buffer=dict(replay_buffer_size=2000000, ), ), |
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), |
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) |
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main_config = EasyDict(main_config) |
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main_config = main_config |
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create_config = dict( |
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env=dict( |
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type='d4rl', |
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import_names=['dizoo.d4rl.envs.d4rl_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='pd', |
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), |
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replay_buffer=dict(type='naive', ), |
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) |
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create_config = EasyDict(create_config) |
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create_config = create_config |