1d04211666
* update from scratch configs * update gym pretraining configs - use fewer epochs * update robomimic pretraining configs - use fewer epochs * allow trajectory plotting in eval agent * add simple vit unet * update avoid pretraining configs - use fewer epochs * update furniture pretraining configs - use same amount of epochs as before * add robomimic diffusion unet pretraining configs * update robomimic finetuning configs - higher lr * add vit unet checkpoint urls * update pretraining and finetuning instructions as configs are updated
59 lines
1.4 KiB
YAML
59 lines
1.4 KiB
YAML
defaults:
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- _self_
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hydra:
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run:
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dir: ${logdir}
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_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
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name: avoid_m2_pre_gaussian_mlp_ta${horizon_steps}
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logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m2/train.npz
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seed: 42
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device: cuda:0
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env: avoid
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mode: d57_r12 # M2, desired modes 5 and 7, required modes 1 and 2
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obs_dim: 4
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action_dim: 2
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horizon_steps: 4
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cond_steps: 1
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wandb:
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entity: ${oc.env:DPPO_WANDB_ENTITY}
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project: d3il-${env}-pretrain
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run: ${now:%H-%M-%S}_${name}
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train:
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n_epochs: 5000
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batch_size: 16
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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_MLP
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mlp_dims: [256, 256, 256] # smaller MLP for less overfitting
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activation_type: ReLU
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residual_style: True
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fixed_std: 0.1
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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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dataset_path: ${train_dataset_path}
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horizon_steps: ${horizon_steps}
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cond_steps: ${cond_steps}
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device: ${device} |