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} # number of patches along one dimension (e.g. 16 for 16x16 patches)