Sheep training flock _ improver

2026-04-25 11:31:39 +01:00
parent 062de676c9
commit fbe76a0d04
3 changed files with 190 additions and 24 deletions
@@ -0,0 +1,109 @@
+"""
+Load a saved run and evaluate the policy at every n_sheep from 1..N.
+Tells you exactly where the curriculum stopped working.
+
+Usage:
+    python eval_per_sheep.py --run-dir runs/ppo_v3
+    python eval_per_sheep.py --run-dir runs/ppo_v3 --max-sheep 10 --episodes 20
+    python eval_per_sheep.py --model runs/ppo_v3/final_model.zip \
+                              --vecnorm runs/ppo_v3/vecnorm.pkl
+"""
+import argparse
+import os
+from copy import deepcopy
+
+import numpy as np
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
+
+from herding_env import HerdingEnv
+from train import _classify, COMPACT_RADIUS
+
+
+def evaluate(model, vn_template, n_sheep, n_episodes, max_steps):
+    raw = DummyVecEnv([lambda: HerdingEnv(n_sheep=n_sheep, max_steps=max_steps)])
+    vn  = VecNormalize(raw, norm_obs=True, norm_reward=False, training=False)
+    vn.obs_rms = deepcopy(vn_template.obs_rms)
+    vn.ret_rms = deepcopy(vn_template.ret_rms)
+
+    failure = {}
+    successes = 0
+    act_mags, min_radii, min_dog_com, min_pen = [], [], [], []
+
+    for _ in range(n_episodes):
+        obs = vn.reset()
+        done = False
+        ep_radius, ep_com_dist, ep_dog_com, ep_act = [], [], [], []
+        while not done:
+            action, _ = model.predict(obs, deterministic=True)
+            obs, _, dones, infos = vn.step(action)
+            done = dones[0]
+            inner = vn.envs[0]
+            com, radius, _ = inner._flock_stats()
+            ep_radius.append(radius)
+            ep_com_dist.append(float(np.linalg.norm(com - inner.PEN_CENTER)))
+            ep_dog_com.append(float(np.linalg.norm(inner.dog_pos - com)))
+            ep_act.append(float(np.linalg.norm(action[0])))
+        npen = infos[0].get("n_penned", 0)
+        success = npen == n_sheep
+        successes += int(success)
+        mode = _classify(ep_radius, ep_com_dist, npen, n_sheep, success)
+        failure[mode] = failure.get(mode, 0) + 1
+        act_mags.extend(ep_act)
+        min_radii.append(min(ep_radius))
+        min_dog_com.append(min(ep_dog_com))
+        min_pen.append(min(ep_com_dist))
+    vn.close()
+
+    return {
+        "n_sheep": n_sheep,
+        "success_rate": successes / n_episodes,
+        "failure": failure,
+        "mean_action": float(np.mean(act_mags)),
+        "mean_min_radius": float(np.mean(min_radii)),
+        "mean_min_dog_com": float(np.mean(min_dog_com)),
+        "mean_min_pen": float(np.mean(min_pen)),
+    }
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--run-dir", type=str, default=None)
+    p.add_argument("--model", type=str, default=None)
+    p.add_argument("--vecnorm", type=str, default=None)
+    p.add_argument("--max-sheep", type=int, default=10)
+    p.add_argument("--episodes", type=int, default=10)
+    p.add_argument("--max-steps", type=int, default=2000)
+    args = p.parse_args()
+
+    if args.run_dir:
+        model_path = os.path.join(args.run_dir, "final_model.zip")
+        if not os.path.exists(model_path):
+            model_path = os.path.join(args.run_dir, "best_model", "best_model.zip")
+        vn_path = os.path.join(args.run_dir, "vecnorm.pkl")
+    else:
+        model_path = args.model
+        vn_path    = args.vecnorm
+
+    print(f"Loading model:   {model_path}")
+    print(f"Loading vecnorm: {vn_path}\n")
+    model = PPO.load(model_path, device="cpu")
+    raw = DummyVecEnv([lambda: HerdingEnv(n_sheep=1, max_steps=args.max_steps)])
+    vn_template = VecNormalize.load(vn_path, raw)
+
+    print(f"{'n_sheep':>7} {'success':>8} {'act':>6} {'min_r':>7} "
+          f"{'dog→com':>8} {'com→pen':>8}  failure breakdown")
+    print("-" * 90)
+    for n in range(1, args.max_sheep + 1):
+        r = evaluate(model, vn_template, n, args.episodes, args.max_steps)
+        fb = " ".join(f"{m}={c}" for m, c in
+                      sorted(r["failure"].items(), key=lambda x: -x[1]))
+        print(f"{n:>7d} {r['success_rate']*100:>6.0f}% "
+              f"{r['mean_action']:>6.2f} "
+              f"{r['mean_min_radius']:>6.2f}m "
+              f"{r['mean_min_dog_com']:>7.2f}m "
+              f"{r['mean_min_pen']:>7.2f}m  {fb}")
+
+
+if __name__ == "__main__":
+    main()