TIR_PROJ/tools/collect_demos.py

"""Collect (obs, action) demonstrations from the sequential teacher.

Runs the sequential algorithm across a grid of (n_sheep, seed) combos
at full difficulty, logs the (observation, action) pair every Nth step,
and saves successful trajectories to a numpy ``.npz`` for behavior
cloning. Failed trajectories are dropped by default — we only want to
teach the policy from good examples.

Usage::

    python -m tools.collect_demos --out training/demos.npz
"""

from __future__ import annotations

import argparse
import os
import sys
import time
from pathlib import Path

_HERE = os.path.dirname(os.path.abspath(__file__))
_PROJECT_ROOT = os.path.normpath(os.path.join(_HERE, ".."))
if _PROJECT_ROOT not in sys.path:
    sys.path.insert(0, _PROJECT_ROOT)

import numpy as np

from herding.active_scan import ActiveScanTeacher
from herding.geometry import PEN_ENTRY
from herding.sequential import compute_action as sequential_action
from herding.strombom import compute_action as strombom_action
from training.herding_env import HerdingEnv


# Base analytic teachers (no scanning). The default at demo-collection
# time wraps these in ActiveScanTeacher, which under LiDAR makes the
# teacher operate on the same partial obs as the student.
TEACHERS = {
    "sequential": sequential_action,
    "strombom": strombom_action,
}


def collect_one(n_sheep: int, seed: int, max_steps: int, subsample: int,
                teacher_fn, frame_stack: int = 1, privileged: bool = False):
    env = HerdingEnv(n_sheep=n_sheep, max_steps=max_steps,
                    difficulty=1.0, seed=seed, frame_stack=frame_stack)
    obs, _ = env.reset(seed=seed)
    obs_list, action_list = [], []
    # Active-scan wrapper: scan first, then run the base teacher on the
    # tracker dict. Reset state per episode so the opening scan kicks in.
    scan_teacher = ActiveScanTeacher(teacher_fn)
    for step in range(max_steps):
        if privileged:
            # Asymmetric "learning by cheating": teacher reads GT, student
            # gets LiDAR obs. Kept available for ablation; default off.
            positions = {f"s{i}": (float(env.sheep_x[i]), float(env.sheep_y[i]))
                         for i in range(env.n_sheep) if not env.sheep_penned[i]}
            if not positions:
                break
            vx, vy, _mode = teacher_fn(
                (env.dog_x, env.dog_y), positions, PEN_ENTRY,
            )
        else:
            # Matched-perception teacher: it sees what the student sees
            # (the tracker dict), with active scanning to fill the
            # tracker before driving.
            positions = env.perceived_positions()
            vx, vy, _mode = scan_teacher(
                (env.dog_x, env.dog_y), env.dog_heading,
                positions, PEN_ENTRY,
            )
        action = np.array([vx, vy], dtype=np.float32)
        if step % subsample == 0:
            obs_list.append(obs.copy())
            action_list.append(action.copy())
        obs, _r, term, trunc, _info = env.step(action)
        if term or trunc:
            break
    success = bool(env.sheep_penned.all())
    return (
        np.asarray(obs_list, dtype=np.float32),
        np.asarray(action_list, dtype=np.float32),
        success,
        env.steps,
    )


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--out", default="training/demos.npz")
    parser.add_argument("--n-sheep-list", default="1,2,3,5,8,10")
    parser.add_argument("--seeds-per-n", type=int, default=15)
    parser.add_argument("--max-steps", type=int, default=30000)
    parser.add_argument("--subsample", type=int, default=5,
                        help="Keep every Nth (obs, action) pair.")
    parser.add_argument("--keep-failures", action="store_true",
                        help="Include partial-success trajectories. Default off.")
    parser.add_argument("--teacher", default="sequential",
                        choices=list(TEACHERS.keys()),
                        help="Which analytic teacher to demonstrate.")
    parser.add_argument("--frame-stack", type=int, default=1,
                        help="K — concatenate the last K env obs into a "
                             "single (32·K)-D vector. Lets a memoryless "
                             "MLP recover temporal info under partial "
                             "LiDAR observability.")
    parser.add_argument("--privileged", action="store_true",
                        help="Teacher reads ground truth (asymmetric BC). "
                             "Default: matched-perception with active scan.")
    args = parser.parse_args()
    teacher_fn = TEACHERS[args.teacher]
    print(f"[demos] teacher: {args.teacher}")

    n_sheep_list = [int(x) for x in args.n_sheep_list.split(",")]
    print(f"[demos] grid: n_sheep={n_sheep_list}, seeds={args.seeds_per_n}, "
          f"max_steps={args.max_steps}, subsample={args.subsample}")

    all_obs, all_actions, all_meta = [], [], []
    t_start = time.time()
    n_success = 0; n_total = 0

    for n in n_sheep_list:
        for seed in range(args.seeds_per_n):
            obs, actions, success, total_steps = collect_one(
                n, seed, args.max_steps, args.subsample, teacher_fn,
                frame_stack=args.frame_stack, privileged=args.privileged,
            )
            n_total += 1
            if success:
                n_success += 1
            keep = success or args.keep_failures
            if keep and len(obs) > 0:
                all_obs.append(obs)
                all_actions.append(actions)
                all_meta.append((n, seed, len(obs), int(success), total_steps))
            tag = "✓" if success else "✗"
            print(f"  [{tag}] n={n:>2d} seed={seed:>2d}  steps={total_steps:>6d}  "
                  f"logged={len(obs):>5d}")

    if not all_obs:
        raise RuntimeError("No trajectories kept — try --keep-failures.")

    obs = np.concatenate(all_obs, axis=0)
    actions = np.concatenate(all_actions, axis=0)
    meta = np.array(all_meta, dtype=np.int32)

    Path(args.out).parent.mkdir(parents=True, exist_ok=True)
    np.savez(args.out, obs=obs, actions=actions, meta=meta)

    elapsed = time.time() - t_start
    print(f"\n=== {n_success}/{n_total} trajectories successful ({100*n_success/n_total:.0f}%) ===")
    print(f"=== {len(obs)} transitions saved to {args.out} ===")
    print(f"=== obs={obs.shape}, actions={actions.shape}, elapsed={elapsed:.0f}s ===")


if __name__ == "__main__":
    main()