| defaults: |
| - env_vars: env_vars |
| - encoder: dino_patch |
| - _self_ |
| - cluster: local |
|
|
| hydra: |
| run: |
| dir: ${env_vars.save_path}/outputs/${env.gym.id}/${now:%Y-%m-%d}/${now:%H-%M-%S} |
| job: |
| chdir: True |
| sweep: |
| dir: ${env_vars.save_path}/outputs/${env.gym.id}/${now:%Y-%m-%d}/${now:%H-%M-%S} |
| subdir: ${hydra.job.num} |
| seed: 42 |
| window_size: 2 |
| goal_window_size: 2 |
| eval_window_size: 2 |
|
|
| num_workers: 0 |
|
|
| batch_size: 32 |
| epochs: 300 |
| eval_freq: 10 |
| eval_on_env_freq: 10 |
| num_env_evals: 100 |
| num_envs: 10 |
| num_final_evals: 100 |
| action_window_size: 3 |
|
|
| pred_horizon: ${eval:'${action_window_size} + ${window_size} - 1'} |
| n_action_steps: ${action_window_size} # Action steps to roll out |
| |
| precompute_embeddings: False |
| |
| goal_dim: 0 |
| wandb: |
| project: patch_policy |
| entity: ${env_vars.wandb_entity} |
| train_fraction: 0.9 |
| |
| device: cuda |
| optim: |
| lr: 1e-4 |
| weight_decay: 0.0 |
| betas: [0.9, 0.999] |
| |
| env: |
| gym: |
| _target_: envs.cube.cube_env.CubeWrapper |
| task_id: 5 |
| id: 'cube' |
| env: |
| _target_: gymnasium.make |
| id: 'cube-double-v1' |
| reward_task_id: 5 |
| max_episode_steps: 300 |
| mode: 'task' |
| act_dim: 5 |
| goal_dim: ${goal_dim} |
| views: 1 |
|
|
| dataset: |
| _target_: datasets.cube.CubeDataset |
| data_directory: ${env_vars.datasets.cube} |
|
|
| data: |
| window_size: ${window_size} |
| action_window_size: ${action_window_size} |
| vqbet_get_future_action_chunk: false |
| goal_conditional: future |
| future_seq_len: ${goal_window_size} |
| min_future_sep: ${action_window_size} |
|
|
| save_ckpt: true |
| save_path: "${env_vars.save_path}/outputs/${env.gym.id}/${now:%Y-%m-%d}/${now:%H-%M-%S}" |
| load_path: null |
|
|
| model: |
| _target_: models.diffusion_policy.diffusion_policy.DiffusionPolicy |
| obs_dim: ${encoder.output_dim} |
| act_dim: ${env.act_dim} |
| goal_dim: ${env.goal_dim} |
| views: ${env.views} |
| obs_horizon: ${window_size} |
| pred_horizon: ${pred_horizon} |
| action_horizon: ${n_action_steps} |
| visual_input: true |
| data_act_scale: 1 |
| lr: ${optim.lr} |
| weight_decay: ${optim.weight_decay} |
| use_transform: null |
| n_patches: ${encoder.n_patches} |
|
|
