"""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)