dppo/cfg/furniture/pretrain/one_leg_low/pre_diffusion_mlp.yaml
ys1087@partner.kit.edu 0424a080c1 feat: HoReKa cluster adaptation and validation
- 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
2025-08-27 14:01:51 +02:00

67 lines
1.7 KiB
YAML

defaults:
- _self_
hydra:
run:
dir: ${logdir}
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
name: ${env}_pre_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
logdir: ${oc.env:DPPO_LOG_DIR}/furniture-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/furniture/${task}_${randomness}/train.npz
seed: 42
device: cuda:0
task: one_leg
randomness: low
env: ${task}_${randomness}_dim
obs_dim: 58
action_dim: 10
denoising_steps: 100
horizon_steps: 8
cond_steps: 1
wandb:
entity: ${oc.env:DPPO_WANDB_ENTITY}
project: dppo-furniture-${task}-${randomness}-pretrain
run: ${now:%H-%M-%S}_${name}
train:
n_epochs: 8000
batch_size: 256
learning_rate: 1e-4
weight_decay: 1e-6
lr_scheduler:
first_cycle_steps: 8000
warmup_steps: 100
min_lr: 1e-5
save_model_freq: 500
model:
_target_: model.diffusion.diffusion.DiffusionModel
predict_epsilon: True
denoised_clip_value: 1.0
network:
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
time_dim: 32
mlp_dims: [1024, 1024, 1024, 1024, 1024, 1024, 1024]
cond_mlp_dims: [512, 64]
use_layernorm: True # needed for larger MLP
residual_style: True
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
horizon_steps: ${horizon_steps}
action_dim: ${action_dim}
horizon_steps: ${horizon_steps}
obs_dim: ${obs_dim}
action_dim: ${action_dim}
denoising_steps: ${denoising_steps}
device: ${device}
ema:
decay: 0.995
train_dataset:
_target_: agent.dataset.sequence.StitchedSequenceDataset
dataset_path: ${train_dataset_path}
horizon_steps: ${horizon_steps}
cond_steps: ${cond_steps}
device: ${device}