85 lines
2.0 KiB
YAML
85 lines
2.0 KiB
YAML
defaults:
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- _self_
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hydra:
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run:
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dir: ${logdir}
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_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
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name: ${env}_pre_gaussian_mlp_img_ta${horizon_steps}
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logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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train_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env}-img/train.npz
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seed: 42
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device: cuda:0
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env: transport
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obs_dim: 18 # proprioception only
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action_dim: 14
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horizon_steps: 8
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cond_steps: 1
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img_cond_steps: 1
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wandb:
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entity: ${oc.env:DPPO_WANDB_ENTITY}
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project: robomimic-${env}-pretrain
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run: ${now:%H-%M-%S}_${name}
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shape_meta:
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obs:
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rgb:
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shape: [3, 96, 96] # not counting img_cond_steps
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state:
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shape: [9]
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action:
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shape: [7]
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train:
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n_epochs: 5000
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batch_size: 256
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learning_rate: 1e-4
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weight_decay: 1e-6
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lr_scheduler:
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first_cycle_steps: 5000
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warmup_steps: 100
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min_lr: 1e-5
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save_model_freq: 500
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model:
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_target_: model.common.gaussian.GaussianModel
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network:
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_target_: model.common.mlp_gaussian.Gaussian_VisionMLP
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backbone:
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_target_: model.common.vit.VitEncoder
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obs_shape: ${shape_meta.obs.rgb.shape}
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num_channel: ${eval:'${shape_meta.obs.rgb.shape[0]} * ${img_cond_steps}'} # each image patch is history concatenated
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cfg:
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patch_size: 8
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depth: 1
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embed_dim: 128
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num_heads: 4
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embed_style: embed2
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embed_norm: 0
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augment: True
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num_img: 2
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spatial_emb: 128
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mlp_dims: [768, 768, 768]
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residual_style: True
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fixed_std: 0.1
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img_cond_steps: ${img_cond_steps}
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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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horizon_steps: ${horizon_steps}
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action_dim: ${action_dim}
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horizon_steps: ${horizon_steps}
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device: ${device}
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ema:
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decay: 0.995
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train_dataset:
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_target_: agent.dataset.sequence.StitchedSequenceDataset
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use_img: True
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dataset_path: ${train_dataset_path}
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horizon_steps: ${horizon_steps}
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max_n_episodes: 100
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cond_steps: ${cond_steps}
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img_cond_steps: ${img_cond_steps}
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device: ${device} |