0424a080c1
- Updated all WandB project names to use dppo- prefix for organization - Added flexible dev testing script for all environments - Created organized dev_tests directory for test scripts - Fixed MuJoCo compilation issues (added GCC compiler flags) - Documented Python 3.10 compatibility and Furniture-Bench limitation - Validated pre-training for Gym, Robomimic, D3IL environments - Updated experiment tracking with validation results - Enhanced README with troubleshooting and setup instructions
66 lines
1.5 KiB
YAML
66 lines
1.5 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_diffusion_agent.TrainDiffusionAgent
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name: ${env}_pre_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
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logdir: ${oc.env:DPPO_LOG_DIR}/gym-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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train_dataset_path: ${oc.env:DPPO_DATA_DIR}/gym/${env}/train.npz
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seed: 42
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device: cuda:0
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env: hopper-medium-v2
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obs_dim: 11
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action_dim: 3
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denoising_steps: 20
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horizon_steps: 4
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cond_steps: 1
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wandb:
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entity: ${oc.env:DPPO_WANDB_ENTITY}
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project: dppo-gym-${env}-pretrain
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run: ${now:%H-%M-%S}_${name}
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train:
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n_epochs: 200
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batch_size: 128
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learning_rate: 1e-3
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weight_decay: 1e-6
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lr_scheduler:
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first_cycle_steps: 200
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warmup_steps: 1
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min_lr: 1e-4
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save_model_freq: 100
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model:
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_target_: model.diffusion.diffusion.DiffusionModel
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predict_epsilon: True
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denoised_clip_value: 1.0
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network:
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_target_: model.diffusion.mlp_diffusion.DiffusionMLP
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horizon_steps: ${horizon_steps}
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action_dim: ${action_dim}
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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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time_dim: 16
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mlp_dims: [512, 512, 512]
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activation_type: ReLU
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out_activation_type: Identity
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use_layernorm: False
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residual_style: True
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horizon_steps: ${horizon_steps}
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obs_dim: ${obs_dim}
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action_dim: ${action_dim}
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denoising_steps: ${denoising_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} |