env:
  name: none
resume: false
device: cuda:1
use_amp: false
seed: 1000
dataset_repo_id: iantc104/rpl_real_peg_in_hole
video_backend: pyav
training:
  offline_steps: 50000
  num_workers: 4
  batch_size: 8
  eval_freq: ???
  log_freq: 100
  save_checkpoint: true
  save_freq: 10000
  online_steps: ???
  online_rollout_n_episodes: 1
  online_rollout_batch_size: 1
  online_steps_between_rollouts: 1
  online_sampling_ratio: 0.5
  online_env_seed: null
  online_buffer_capacity: null
  online_buffer_seed_size: 0
  do_online_rollout_async: false
  image_transforms:
    enable: false
    max_num_transforms: 3
    random_order: false
    brightness:
      weight: 1
      min_max:
      - 0.8
      - 1.2
    contrast:
      weight: 1
      min_max:
      - 0.8
      - 1.2
    saturation:
      weight: 1
      min_max:
      - 0.5
      - 1.5
    hue:
      weight: 1
      min_max:
      - -0.05
      - 0.05
    sharpness:
      weight: 1
      min_max:
      - 0.8
      - 1.2
  num_episodes: 40
  lr: 1.0e-05
  weight_decay: 0.0001
  grad_clip_norm: 10
  delta_timestamps:
    observation.images.wrist:
    - -2.0
    - -1.0
    - 0.0
    observation.state:
    - -2.0
    - -1.0
    - 0.0
eval:
  n_episodes: 1
  batch_size: 1
  use_async_envs: false
wandb:
  enable: true
  disable_artifact: false
  project: rpl_sim
  notes: ''
fps: 1
policy:
  name: rnd
  n_obs_steps: 3
  fps: ${fps}
  input_shapes:
    observation.images.wrist:
    - 3
    - 120
    - 160
    observation.state:
    - 22
  input_normalization_modes:
    observation.images.wrist: mean_std
    observation.state: mean_std
  predictor_cnn_out_channels:
  - 16
  - 32
  - 64
  predictor_cnn_kernel_size:
  - 10
  - 6
  - 4
  predictor_cnn_stride:
  - 5
  - 3
  - 2
  predictor_cnn_padding:
  - 0
  - 0
  - 0
  predictor_cnn_use_batchnorm: false
  predictor_cnn_use_maxpool: false
  predictor_cnn_use_spatial_softmax: true
  predictor_cnn_spatial_softmax_num_keypoints: 32
  predictor_mlp_hidden_sizes:
  - 512
  - 512
  predictor_activation: ReLU
  target_cnn_out_channels:
  - 16
  - 32
  - 64
  target_cnn_kernel_size:
  - 10
  - 6
  - 4
  target_cnn_stride:
  - 5
  - 3
  - 2
  target_cnn_padding:
  - 0
  - 0
  - 0
  target_cnn_use_batchnorm: false
  target_cnn_use_maxpool: false
  target_cnn_use_spatial_softmax: true
  target_cnn_spatial_softmax_num_keypoints: 32
  target_mlp_hidden_sizes:
  - 512
  - 512
  target_activation: ReLU
  dim_output: 512