# @package _global_

offline_steps: 80000
online_steps: 0

eval_episodes: 1
eval_freq: 10000
save_freq: 100000
log_freq: 250

horizon: 100
n_obs_steps: 1
# when temporal_agg=False, n_action_steps=horizon
n_action_steps: ${horizon}

policy:
  name: act

  pretrained_model_path:

  lr: 1e-5
  lr_backbone: 1e-5
  pretrained_backbone: true
  weight_decay: 1e-4
  grad_clip_norm: 10
  backbone: resnet18
  horizon: ${horizon} # chunk_size
  kl_weight: 10
  d_model: 512
  dim_feedforward: 3200
  vae_enc_layers: 4
  enc_layers: 4
  dec_layers: 1
  num_heads: 8
  #camera_names: [top, front_close, left_pillar, right_pillar]
  camera_names: [top]
  dilation: false
  dropout: 0.1
  pre_norm: false
  activation: relu
  latent_dim: 32

  use_vae: true

  batch_size: 8

  per_alpha: 0.6
  per_beta: 0.4

  balanced_sampling: false
  utd: 1

  n_obs_steps: ${n_obs_steps}

  temporal_agg: false

  state_dim: 14
  action_dim: 14

  image_normalization:
    mean: [0.485, 0.456, 0.406]
    std: [0.229, 0.224, 0.225]

  delta_timestamps:
    observation.images.top: [0.0]
    observation.state: [0.0]
    action: "[i / ${fps} for i in range(${horizon})]"