configs/final_ucl_gpu.yaml

# =============================================================================
# UCL 3090 Ti GPU — final paper run config
# =============================================================================
#
# This single config drives BOTH the final DAgger run and the
# compute-matched offline BC baseline used for the paper comparison.
#
#   --mode dagger    → reproduces the iter600 DAgger checkpoint recipe
#   --mode offline   → trains a fair offline BC baseline against it
#
# All training hyperparameters are IDENTICAL to `final_qmul_gpu.yaml`
# so cross-cluster runs produce directly comparable results. The only
# differences are hardware-specific perf knobs (collection workers).
# See the QMUL config header for the full fairness analysis.
#
# ── Hardware ─────────────────────────────────────────────────────────
#
# UCL 3090 Ti — 24 GB VRAM. The 4-layer × 256-dim model with
# batch=2048 and AMP fits with comfortable headroom (~6-8 GB peak).
# Lower core count than the QMUL cluster, so collection workers
# capped at 8.

# ── 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 (matches checkpoint) ───────────────────────────────────────
n_embd: 256
n_head: 4
n_layer: 4
n_global_tokens: 8
seq_len: 64
global_gate_init: -3.0
dropout: 0.0
ema_decay: 0.999

# ── Diffusion (MDLM) — matches checkpoint ────────────────────────────
noise_schedule: linear
num_diffusion_steps: 100
loss_weight_clip: 1000.0
label_smoothing: 0.0
use_importance_weighting: false
eta: 0.15
remask_strategy: conf

# ── Inference / sampling — matches checkpoint ────────────────────────
diffusion_steps_eval: 10
diffusion_steps_collect: 5
temperature: 0.5
top_k: 4
replan_every: 16
physics_aware_sampling: false

# ── Shared training budget (DAgger only) ─────────────────────────────
# 5.65M env-steps reproduces the env-step budget consumed at iter600
# of the original DAgger run. Calibrated against a real DAgger run
# with the same recipe (see QMUL config header for the full derivation).
# The earlier 3M figure was based on the buggy single-episode env-step
# accounting in `online.py:155-169` — fixed in the same commit as this
# config bump. Used by `--mode dagger` only. Offline mode bypasses
# this via `offline_total_grad_steps` below.
total_timesteps: 5650000

# Eval/checkpoint cadence in env-step units (DAgger mode).
# Scaled with the corrected total_timesteps so the run still produces
# ~12 ID/OOD evals and ~6 checkpoints over its full duration.
id_eval_every_timesteps: 470000
ood_eval_every_timesteps: 470000
checkpoint_every_timesteps: 940000

# Final-eval episode count (used by both ID/OOD eval triggers and
# checkpoint-time evals; matches the original DAgger run).
eval_episodes_per_env: 50
checkpoint_eval_episodes: 50
weight_decay: 0.0001
aux_loss_weight: 0.5

# ── DAgger (matches checkpoint_final/online/config_iter600.yaml) ─────
dagger_lr: 0.00003
dagger_batch_size: 2048
dagger_grad_clip: 1.0
buffer_capacity: 10000
episodes_per_iteration: 30
grad_steps_per_iteration: 100
efficiency_multiplier: 1.5
curriculum_queue_size: 100
curriculum_preseed: true

# ── Offline BC (compute-matched fair baseline) ───────────────────────
# Per the fairness analysis (see QMUL config header):
#   * Same gradient compute as DAgger (60k AdamW updates × 2048 batch)
#   * Same model, diffusion, weight_decay, grad_clip, aux_loss
#   * BC-tuned LR + cosine schedule (best practice from-scratch)
#   * Eval/checkpoint counts matched to DAgger via grad-step overrides
#
# `offline_batch_size: 2048` is matched to DAgger (NOT the 4096 the
# previous UCL config used) so per-update SNR is identical between
# modes — this is the cleanest apples-to-apples optimisation
# comparison. The 24 GB VRAM can hold a larger batch but using one
# would confound the comparison.
offline_lr: 0.0003
offline_batch_size: 2048
offline_grad_clip: 1.0

# Compute pin: 60,000 AdamW updates = exactly DAgger@iter600.
offline_total_grad_steps: 60000

# Eval cadence: 5,000 grad steps → 12 evals (matches DAgger eval count).
offline_eval_every_grad_steps: 5000

# Checkpoint cadence: 10,000 grad steps → 6 checkpoints (matches DAgger).
offline_checkpoint_every_grad_steps: 10000

# Buffer cap for offline mode only — must hold the full pre-collected
# dataset (~1M sliding windows from 20k oracle trajectories). DAgger's
# `buffer_capacity: 10000` would silently FIFO-evict 99% of the data.
offline_buffer_capacity: 1500000

# ── Performance (cluster-tuned for 3090 Ti) ──────────────────────────
use_amp: true
torch_compile: true
num_collection_workers: 8

# ── Data collection (for offline BC dataset) ─────────────────────────
# 5000 eps × 4 ID envs = 20k oracle trajectories. Strictly more than
# the ~7k unique trajectories DAgger had in its filtered buffer at
# iter600 — offline always gets a richer pre-collected pool, which is
# the standard fairness asymmetry in BC vs DAgger comparisons.
collect_episodes_per_env: 5000
collect_num_workers: 8
collect_output: data/oracle_bc_ucl.pt

# ── Checkpointing & Logging ──────────────────────────────────────────
checkpoint_dir: checkpoints_ucl
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
# wandb_resume_id intentionally omitted — fresh runs by default.
# Override on the CLI (`wandb_resume_id=...`) to continue an existing run.
offline_log_every: 50
seed: null