| # ── Environments ────────────────────────────────────────────────────── | |
| id_envs: | |
| - MiniHack-Room-Random-5x5-v0 | |
| - MiniHack-Room-Random-15x15-v0 | |
| - MiniHack-Corridor-R2-v0 | |
| - MiniHack-MazeWalk-9x9-v0 | |
| ood_envs: | |
| - MiniHack-Room-Dark-15x15-v0 | |
| - MiniHack-Corridor-R5-v0 | |
| - MiniHack-MazeWalk-45x19-v0 | |
| crop_size: 9 | |
| map_h: 21 | |
| map_w: 79 | |
| action_dim: 12 | |
| mask_token: 12 | |
| pad_token: 13 | |
| # ── Model ───────────────────────────────────────────────────────────── | |
| n_embd: 256 | |
| n_head: 4 | |
| n_layer: 4 | |
| n_global_tokens: 8 | |
| seq_len: 64 | |
| global_gate_init: -3.0 | |
| # Transformer dropout. 0.0 is deliberate — discrete diffusion forward masking | |
| # already regularises; dropout on top is redundant. | |
| dropout: 0.0 | |
| ema_decay: 0.999 | |
| # ── Diffusion (MDLM) ───────────────────────────────────────────────── | |
| noise_schedule: linear | |
| num_diffusion_steps: 100 | |
| loss_weight_clip: 1000.0 | |
| label_smoothing: 0.0 | |
| # Use SUBS importance weighting w(t) in loss. Off by default (flat average | |
| # matching reference). Enable for MDLM ELBO experiments. | |
| use_importance_weighting: false | |
| # ReMDM stochastic remask base fraction | |
| eta: 0.18 | |
| # Remasking strategy: rescale | cap | conf | |
| remask_strategy: conf | |
| # ── Inference ───────────────────────────────────────────────────────── | |
| # Number of reverse denoising steps at inference. | |
| # Reference uses 5 (aggressive). Higher = better quality, slower. | |
| diffusion_steps_eval: 10 | |
| diffusion_steps_collect: 5 | |
| temperature: 0.5 | |
| top_k: 4 | |
| replan_every: 16 | |
| # Soft-penalise hazardous cardinal actions during stochastic sampling. | |
| # Not active in the reference evaluation pipeline; off by default. | |
| physics_aware_sampling: false | |
| # ── Training budget (unified) ──────────────────────────────────────── | |
| total_timesteps: 20000000 | |
| id_eval_every_timesteps: 250000 | |
| ood_eval_every_timesteps: 250000 | |
| checkpoint_every_timesteps: 1250000 | |
| # ── Offline BC ──────────────────────────────────────────────────────── | |
| offline_lr: 0.0003 | |
| offline_batch_size: 6144 | |
| offline_grad_clip: 1.0 | |
| aux_loss_weight: 0.5 | |
| # ── DAgger ──────────────────────────────────────────────────────────── | |
| dagger_lr: 0.00003 | |
| dagger_batch_size: 6144 | |
| dagger_grad_clip: 1.0 | |
| weight_decay: 0.0001 | |
| buffer_capacity: 10000 | |
| episodes_per_iteration: 30 | |
| grad_steps_per_iteration: 100 | |
| efficiency_multiplier: 1.5 | |
| curriculum_queue_size: 100 | |
| # Pre-seed curriculum queues with 50/50 prior for uniform early sampling. | |
| curriculum_preseed: true | |
| eval_episodes_per_env: 50 | |
| checkpoint_eval_episodes: 50 | |
| # ── Performance ────────────────────────────────────────────────────── | |
| # Mixed-precision (FP16) training via torch.cuda.amp. | |
| # Speeds up forward/backward ~1.5-2x on GPU. No effect on CPU. | |
| use_amp: true | |
| # torch.compile the model for fused kernels (experimental). | |
| # May cause slow first iteration due to compilation. No effect on CPU. | |
| torch_compile: true | |
| # Number of parallel workers for DAgger episode collection. | |
| # 0 = sequential (reference behaviour). Recommended: 4-8 on multi-core. | |
| num_collection_workers: 8 | |
| # ── Checkpointing & Logging ────────────────────────────────────────── | |
| checkpoint_dir: checkpoints_ucl_learning_behaviour | |
| save_policy: true | |
| hub_run_id: null | |
| hub_repo_id: null | |
| use_wandb: true | |
| wandb_project: remdm-minihack | |
| wandb_entity: "mathis-weil-university-college-london-ucl-" | |
| wandb_run_name: null | |
| offline_log_every: 10 | |
| seed: null | |