# @package _global_

policy:
  name: act
  trainer: policy

  action_horizon: 8
  pred_horizon: 16
  obs_horizon: 1
  
  observer:
    name: resnet18
    tokenize: false

  transformer:
    pre_norm: false
    dim_model: 512
    n_heads: 8
    dim_feedforward: 3200
    feedforward_activation: "relu"
    n_encoder_layers: 4
    n_decoder_layers: 1
    dropout: 0.1
    use_vae: true
    latent_dim: 32
    n_vae_encoder_layers: 4

  act:
    kl_weight: 10.0

  optimizer:
    lr: 1e-5
    betas: [0.9, 0.999]
    eps: 1e-8
    weight_decay: 1e-4
    # Use a smaller learning rate for the ResNet backbone
    backbone_lr_scale: 1.0

  scheduler:
    name: "cosine"
    warmup_steps: 2000

wandb:
  tags:
    - ${policy.name}
    - ${policy.trainer}

train:
  steps: 200000

val:
  val_online_freq: 2000
