90 lines
2.0 KiB
YAML
90 lines
2.0 KiB
YAML
# @package _global_
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# Train with:
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#
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# python lerobot/scripts/train.py \
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# env=pusht \
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# +dataset=lerobot/pusht_keypoints
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seed: 1
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dataset_repo_id: lerobot/pusht_keypoints
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training:
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offline_steps: 0
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# Offline training dataloader
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num_workers: 4
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batch_size: 128
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grad_clip_norm: 10.0
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lr: 3e-4
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eval_freq: 50000
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log_freq: 500
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save_freq: 50000
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online_steps: 1000000
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online_rollout_n_episodes: 10
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online_rollout_batch_size: 10
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online_steps_between_rollouts: 1000
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online_sampling_ratio: 1.0
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online_env_seed: 10000
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online_buffer_capacity: 40000
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online_buffer_seed_size: 0
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do_online_rollout_async: false
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delta_timestamps:
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observation.environment_state: "[i / ${fps} for i in range(${policy.horizon} + 1)]"
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observation.state: "[i / ${fps} for i in range(${policy.horizon} + 1)]"
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action: "[i / ${fps} for i in range(${policy.horizon})]"
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next.reward: "[i / ${fps} for i in range(${policy.horizon})]"
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policy:
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name: sac
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pretrained_model_path:
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# Input / output structure.
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n_action_repeats: 1
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horizon: 5
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n_action_steps: 5
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input_shapes:
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# TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
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observation.environment_state: [16]
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observation.state: ["${env.state_dim}"]
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output_shapes:
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action: ["${env.action_dim}"]
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# Normalization / Unnormalization
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input_normalization_modes:
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observation.environment_state: min_max
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observation.state: min_max
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output_normalization_modes:
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action: min_max
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# Architecture / modeling.
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# Neural networks.
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# image_encoder_hidden_dim: 32
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discount: 0.99
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temperature_init: 1.0
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num_critics: 2
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num_subsample_critics: None
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critic_lr: 3e-4
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actor_lr: 3e-4
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temperature_lr: 3e-4
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critic_target_update_weight: 0.005
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utd_ratio: 2
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# # Loss coefficients.
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# reward_coeff: 0.5
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# expectile_weight: 0.9
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# value_coeff: 0.1
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# consistency_coeff: 20.0
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# advantage_scaling: 3.0
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# pi_coeff: 0.5
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# temporal_decay_coeff: 0.5
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# # Target model.
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# target_model_momentum: 0.995
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