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53 行
1.4 KiB
53 行
1.4 KiB
behaviors:
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CrawlerStatic:
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trainer_type: sac_transfer
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hyperparameters:
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learning_rate: 0.0003
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learning_rate_schedule: linear
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model_schedule: constant
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batch_size: 256
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buffer_size: 3000000
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buffer_init_steps: 2000
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tau: 0.005
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steps_per_update: 20.0
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save_replay_buffer: false
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init_entcoef: 1.0
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reward_signal_steps_per_update: 20.0
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encoder_layers: 2
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policy_layers: 3
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forward_layers: 2
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value_layers: 2
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feature_size: 512
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action_layers: 2
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action_feature_size: 128
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separate_policy_train: true
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separate_policy_net: true
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separate_model_train: true
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# separate_value_net: true
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reuse_encoder: true
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in_epoch_alter: false
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in_batch_alter: true
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use_op_buffer: false
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use_var_predict: true
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with_prior: false
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predict_return: true
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use_transfer: true
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load_model: true
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train_model: false
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load_action: true
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train_action: false
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transfer_path: "results/oldcs-sep-f512/CrawlerStatic"
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network_settings:
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normalize: true
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hidden_units: 512
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num_layers: 3
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vis_encode_type: simple
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reward_signals:
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extrinsic:
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gamma: 0.995
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strength: 1.0
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keep_checkpoints: 5
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max_steps: 3000000
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time_horizon: 1000
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summary_freq: 30000
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threaded: true
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