| |
| """Structural checks and optional live evaluation for the 14-parameter LunarLander policy. |
| |
| python verify.py # structural checks only (torch) |
| python verify.py --episodes 100 # + live Gymnasium evaluation (60 seeds x N episodes) |
| |
| Reference numbers (60 seeds x 100 episodes): mean 264.06, 60/60 seeds >= 200, 0.35% crashes. |
| """ |
| from __future__ import annotations |
|
|
| import argparse |
| import os |
|
|
| import numpy as np |
|
|
| from model import TinyLunarPolicy, OBS_DIM, N_ACTIONS, N_PARAMS |
|
|
| |
| EVAL_SEEDS = [ |
| 40122, 42940, 51305, 55316, 60944, 61197, 93978, 96327, 103680, 157789, |
| 184578, 197504, 208678, 250957, 289579, 296198, 296953, 309957, 318961, 323741, |
| 349217, 350692, 373376, 382648, 383930, 385293, 393420, 400382, 414089, 450829, |
| 467030, 482346, 490224, 498162, 501685, 515927, 535096, 543079, 544042, 553850, |
| 555722, 571111, 615137, 634353, 642133, 660500, 665066, 684081, 732008, 743613, |
| 800434, 816622, 819515, 839392, 845498, 852069, 852799, 867535, 894908, 898516, |
| ] |
|
|
|
|
| def structural_checks(path: str) -> TinyLunarPolicy: |
| m = TinyLunarPolicy.from_safetensors(path) |
| W = m.numpy_weights() |
| print(f"parameters : {m.n_params} (expected {N_PARAMS})") |
| print(f"materialised W shape : {W.shape} (expected ({OBS_DIM}, {N_ACTIONS}))") |
| assert m.n_params == N_PARAMS |
|
|
| rng = np.random.default_rng(0) |
| O = rng.standard_normal((2000, OBS_DIM)).astype(np.float32) |
| ref = np.argmax(O @ W, axis=1) |
| got = np.asarray([m.act(o) for o in O[:500]]) |
| assert (ref[:500] == got).all(), "module disagrees with argmax(obs @ W)" |
| print(f"argmax(obs @ W) == act : {(ref[:500] == got).sum()}/500 OK") |
| return m |
|
|
|
|
| def live_eval(m: TinyLunarPolicy, seeds, n_ep: int, env_id: str = "LunarLander-v3"): |
| import gymnasium as gym |
| env = gym.make(env_id) |
| means, lands, crashes = [], 0, 0 |
| for s in seeds: |
| rs = [] |
| for ep in range(n_ep): |
| o, _ = env.reset(seed=int(s) * 1000 + ep) |
| done, tot, last, term, trunc = False, 0.0, 0.0, False, False |
| while not done: |
| o, r, term, trunc, _ = env.step(m.act(o)) |
| tot += r |
| last = r |
| done = term or trunc |
| rs.append(tot) |
| lands += int(term and not trunc and last == 100.0) |
| crashes += int(term and not trunc and last == -100.0) |
| means.append(float(np.mean(rs))) |
| means = np.asarray(means) |
| n = len(seeds) * n_ep |
| print(f"{len(seeds)} seeds x {n_ep} ep = {n} episodes") |
| print(f"mean={means.mean():.3f} std(seed means)={means.std():.3f} " |
| f"min={means.min():.2f} max={means.max():.2f}") |
| print(f"seeds with mean>=200: {int((means >= 200).sum())}/{len(means)}") |
| print(f"landing rate={lands / n:.4f} crash rate={crashes / n:.4f}") |
| return means |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--weights", default=os.path.join(os.path.dirname(os.path.abspath(__file__)), |
| "model.safetensors")) |
| ap.add_argument("--episodes", type=int, default=0, help="episodes per seed (0 = skip)") |
| ap.add_argument("--seeds", type=int, default=60) |
| ap.add_argument("--env-id", default="LunarLander-v3") |
| a = ap.parse_args() |
| m = structural_checks(a.weights) |
| if a.episodes > 0: |
| live_eval(m, EVAL_SEEDS[: a.seeds], a.episodes, a.env_id) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
