TIR_PROJ/training

Training pipeline

Behavior cloning of analytic herding teachers into a neural-network policy that runs under LiDAR perception in Webots.

sim demos (active-scan teacher on tracker output, K=4 frame stack)
    │
    ▼
bc_pretrain.py  ──►  runs/bc   (BC baseline)
    │
    ▼  KL-regularised PPO fine-tune (training/train_ppo.py)
    │
runs/rl         (deployed `rl` mode)

# optional branch — kept for reference, not deployed:
runs/bc_dagger     (Webots-grounded DAgger refinement, useful if a
                    modified world breaks sim-to-real transfer)

Files

herding_env.py     — Gymnasium env (LiDAR raycast + tracker by default)
bc_pretrain.py     — MSE + cosine BC of (obs, action) demos into MlpPolicy
eval.py            — analytic teachers + BC policies, full n=1..10 grid
parity_test.py     — shape / determinism / baseline smoke test
runs/              — checkpoints (most are .gitignored; the deployed
                      ones are whitelisted in the top-level .gitignore)

Setup

pip install -r requirements.txt

CPU is the default and recommended device — SB3 PPO with an MLP policy of this size runs faster on CPU than GPU because the bottleneck is rollout collection, not gradient compute.

The BC pipeline

# 1. Sim demos with the active-scan + Strömbom teacher under LiDAR
#    perception. K=4 frame stack so the MLP has temporal context.
python -m tools.collect_demos --teacher strombom \
    --out demos.npz --seeds-per-n 15 --subsample 3 --frame-stack 4

# 2. Behavior-clone.
python -m training.bc_pretrain --demos demos.npz \
    --out runs/bc --epochs 60 --net-arch 512,512

# 3. Evaluate.
python -m training.eval --policy runs/bc \
    --max-flock 10 --max-steps 8000 --n-seeds 5

bc_pretrain.py saves the best-val_cos snapshot, not the final epoch — multi-modal teachers make training noisy and the last epoch is often worse than an earlier one.

DAgger from Webots

Sim-only BC plateaus because the env's 2D raycast can't reproduce all the false-positive clusters Webots generates from real geometry. The fix is to collect (obs, teacher_action) pairs from inside Webots:

# Headless DAgger collection: 5 flock sizes × 3 runs each.
tools/auto_dagger.sh 3 60

# Merge with the sim baseline + retrain.
python -m tools.dagger_merge_train --out runs/bc_dagger

Iterate by re-running collection with the new student in the driver's seat:

HERDING_POLICY_DIR=$PWD/training/runs/bc_dagger \
HERDING_DAGGER_DRIVER=student \
tools/auto_dagger.sh 3 60
python -m tools.dagger_merge_train --out runs/bc_dagger

Available analytic teachers

Name	What it does	Notes
strombom	Canonical Strömbom — collect when flock is scattered, drive CoM otherwise	Default; works well for n=1–10 under tight cohesion
sequential	Pick the sheep closest to the pen and drive only it	Alternative; needs loose-cohesion regime

Both are wrapped at demo-collection time in herding/active_scan.py:ActiveScanTeacher, which adds an opening in-place rotation, walk-to-centre when the LiDAR sees nothing, and near-sheep speed modulation (the same modulation herding/control.py applies to every dog mode at inference).

Evaluating analytic teachers directly

python -m training.eval --policy strombom    --max-flock 10 --max-steps 8000 --n-seeds 5
python -m training.eval --policy sequential  --max-flock 10 --max-steps 8000 --n-seeds 5

Webots inference

tools/run_webots.sh 10 rl

The dog controller loads runs/bc for bc mode and runs/rl for rl mode. Override with HERDING_POLICY_DIR=… for a specific checkpoint.