File size: 2,440 Bytes
7d3c387 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | """v2 统一形状 RL 池子的评测。不碰 RPG 里 v1 的评测链。
用法: rl_eval_v2.py <alg> <env> <ckpt...> (ckpt 也可以是 'POOLMEAN:<池子目录>')
"""
import glob, os, sys
import numpy as np, torch
sys.path.insert(0, "/data/chl0045/project/benchmark_v2")
import gymnasium as gym # 已被 sitecustomize 打过补丁(补观测维/Discrete(4))
from models.rl_actor_critic import create_actor_critic
from models.rl_qnet import create_qnet
alg, env_id = sys.argv[1], sys.argv[2]
targets = sys.argv[3:]
THR = {"Acrobot-v1": -100.0, "LunarLander-v3": 200.0}[env_id]
def build():
if alg == "ppo": return create_actor_critic(obs_dim=8, n_actions=4, hidden_dim=64)
return create_qnet(obs_dim=8, n_actions=4, hidden_dim=64)
def load_sd(p):
sd = torch.load(p, map_location="cpu")
return sd.get("model_state_dict", sd) if isinstance(sd, dict) else sd
def pool_mean(d):
fs = sorted(glob.glob(d + "/**/*.pth", recursive=True))
acc = None
for f in fs:
sd = load_sd(f)
if acc is None: acc = {k: v.double().clone() for k, v in sd.items()}
else:
for k in acc: acc[k] += sd[k].double()
return {k: (v / len(fs)).float() for k, v in acc.items()}, len(fs)
@torch.no_grad()
def run(sd, n_ep=int(__import__("os").environ.get("RL_EPISODES", 10)), seed0=12345):
m = build(); m.load_state_dict(sd, strict=True); m.eval()
head = m.actor if hasattr(m, "actor") else m.net
e = gym.make(env_id)
Rs, Ls = [], []
for i in range(n_ep):
o, _ = e.reset(seed=seed0 + i); done = False; R = 0.0; L = 0
while not done:
t = torch.from_numpy(np.asarray(o, dtype=np.float32)).unsqueeze(0)
a = int(head(t).argmax(dim=-1).item())
o, r, te, tr, _ = e.step(a); R += r; L += 1; done = te or tr
Rs.append(R); Ls.append(L)
return float(np.mean(Rs)), float(np.std(Rs)), float(np.mean([r >= THR for r in Rs])), float(np.mean(Ls))
out = []
for t in targets:
if t.startswith("POOLMEAN:"):
sd, n = pool_mean(t.split(":", 1)[1]); name = f"池均值({n}个)"
else:
sd = load_sd(t); name = os.path.basename(t)
R, S, succ, L = run(sd)
out.append(R)
print(f" {name:<26} R = {R:+9.2f} (std {S:6.2f}) succ={succ:.2f} len={L:.0f}", flush=True)
if len(out) > 1:
print(f" >>> 汇总 n={len(out)} R = {np.mean(out):+.2f} ± {np.std(out, ddof=1):.2f}", flush=True)
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