defaults:
  - _self_
hydra:
  run:
    dir: ${logdir}
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent

name: ${env}_pre_gaussian_mlp_img_ta${horizon_steps}
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env}-img/train.npz

seed: 42
device: cuda:0
env: square
obs_dim: 9  # proprioception only
action_dim: 7
horizon_steps: 4
cond_steps: 1
img_cond_steps: 1

wandb:
  entity: ${oc.env:DPPO_WANDB_ENTITY}
  project: robomimic-${env}-pretrain
  run: ${now:%H-%M-%S}_${name}

shape_meta:
  obs:
    rgb:
      shape: [3, 96, 96]
    state:
      shape: [9]
  action: 
    shape: [7]

train:
  n_epochs: 4000
  batch_size: 256
  learning_rate: 1e-4
  weight_decay: 1e-6
  lr_scheduler:
    first_cycle_steps: 5000
    warmup_steps: 100
    min_lr: 1e-5
  save_model_freq: 500

model:
  _target_: model.common.gaussian.GaussianModel
  network:
    _target_: model.common.mlp_gaussian.Gaussian_VisionMLP
    backbone:
      _target_: model.common.vit.VitEncoder
      obs_shape: ${shape_meta.obs.rgb.shape}
      num_channel: ${eval:'${shape_meta.obs.rgb.shape[0]} * ${img_cond_steps}'} # each image patch is history concatenated
      cfg:
        patch_size: 8
        depth: 1
        embed_dim: 128
        num_heads: 4
        embed_style: embed2
        embed_norm: 0
    augment: True
    spatial_emb: 128
    mlp_dims: [768, 768, 768]
    residual_style: True
    fixed_std: 0.1
    img_cond_steps: ${img_cond_steps}
    cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
    horizon_steps: ${horizon_steps}
    action_dim: ${action_dim}
  horizon_steps: ${horizon_steps}
  device: ${device}

ema:
  decay: 0.995

train_dataset:
  _target_: agent.dataset.sequence.StitchedSequenceDataset
  use_img: True
  dataset_path: ${train_dataset_path}
  horizon_steps: ${horizon_steps}
  max_n_episodes: 100
  cond_steps: ${cond_steps}
  img_cond_steps: ${img_cond_steps}
  device: ${device}