mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-23 12:40:08 +00:00
Michel Aractingi
ff47c0b0d3
- Fixed big issue in the normalization of the actions in the `forward` function of the critic -- remove the `torch.no_grad` decorator in `normalize.py` in the normalization function - Fixed performance issue to boost the optimization frequency by setting the storage device to be the same as the device of learning. Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
109 lines
3.0 KiB
YAML
109 lines
3.0 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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# +dataset=lerobot/pusht_keypoints
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# env=pusht \
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# env.gym.obs_type=environment_state_agent_pos \
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seed: 1
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dataset_repo_id: null
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training:
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# Offline training dataloader
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num_workers: 4
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# batch_size: 256
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batch_size: 512
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grad_clip_norm: 10.0
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lr: 3e-4
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eval_freq: 2500
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log_freq: 10
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save_freq: 2000000
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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: 1000000
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online_buffer_seed_size: 0
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online_step_before_learning: 5000
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do_online_rollout_async: false
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policy_update_freq: 1
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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: 1
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n_action_steps: 1
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shared_encoder: true
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vision_encoder_name: null
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# vision_encoder_name: "helper2424/resnet10"
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# freeze_vision_encoder: true
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freeze_vision_encoder: false
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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.state: ["${env.state_dim}"]
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observation.image: [3, 128, 128]
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output_shapes:
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action: [7]
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# Normalization / Unnormalization
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input_normalization_modes:
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observation.state: min_max
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input_normalization_params:
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observation.state:
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min: [-1.9361e+00, -7.7640e-01, -7.7094e-01, -2.9709e+00, -8.5656e-01,
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1.0764e+00, -1.2680e+00, 0.0000e+00, 0.0000e+00, -9.3448e+00,
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-3.3828e+00, -3.8420e+00, -5.2553e+00, -3.4154e+00, -6.5082e+00,
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-6.0500e+00, -8.7193e+00, -8.2337e+00, -3.4650e-01, -4.9441e-01,
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8.3516e-03, -3.1114e-01, -9.9700e-01, -2.3471e-01, -2.7137e-01]
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max: [ 0.8644, 1.4306, 1.8520, -0.7578, 0.9508, 3.4901, 1.9381, 0.0400,
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0.0400, 5.0885, 4.7156, 7.9393, 7.9100, 2.9796, 5.7720, 4.7163,
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7.8145, 9.7415, 0.2422, 0.4505, 0.6306, 0.2622, 1.0000, 0.5135,
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0.4001]
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output_normalization_modes:
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action: min_max
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output_normalization_params:
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action:
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min: [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0]
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max: [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]
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output_normalization_shapes:
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action: [7]
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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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discount: 0.80
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temperature_init: 1.0
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num_critics: 2 #10
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num_subsample_critics: null
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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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critic_target_update_weight: 0.01
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utd_ratio: 2 # 10
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actor_learner_config:
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actor_ip: "127.0.0.1"
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port: 50051
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