"""Training loop for ST-GFN and 10 baselines across 4 environments.

Vectorized rollouts: all B trajectories in a batch advance together so each
timestep costs one network forward pass.

Methods implemented (paper Sec. 4 / App. C.1.1):
  Standard GFlowNets      : tb, fm, subtb, db
  Stochastic GFlowNets    : eflownet, stochastic_gfn
  Exploration-enhanced    : tb_rnd, tb_novelty, tb_icm, tb_cv
  Ours                    : stgfn  (+ ablations stgfn_no_spectral, stgfn_no_intrinsic)
"""
from __future__ import annotations

import argparse
import json
import math
import os
import time
from collections import defaultdict

import numpy as np
import torch
import torch.nn.functional as F

from envs import ENVS
from models import RFF, GFNNet, RNDNet, ICMNet, AutocorrIntrinsic

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

MAX_T = {"bitsequence": 8, "hypergrid": 70, "tictactoe": 9, "singlecell_proxy": 3}


def max_horizon(env_name, cfg):
    """Longest possible trajectory, so rollouts are never truncated.

    HyperGrid needs 2*(size-1) moves to reach the far corner plus one stop
    action; a fixed cap silently truncates trajectories on larger grids and
    quietly corrupts every terminal-state metric.
    """
    if env_name == "hypergrid":
        return 2 * (cfg["grid_size"] - 1) + 2
    if env_name == "bitsequence":
        return cfg["bit_length"]
    if env_name == "singlecell_proxy":
        return cfg["k_genes"]
    return MAX_T[env_name]


def backward_mask(env, state, n_actions):
    """Actions that could have produced `state` from a valid parent.

    A mask with a single entry means the state has a unique parent, so
    log P_B(parent|state) = 0 and the backward term drops out of the TB
    residual. Getting this right matters: marking every action as a possible
    parent lets the learned P_B cancel P_F in the residual, which silently
    removes all reward signal from the objective.
    """
    m = np.zeros(n_actions, dtype=np.float32)
    if env.name == "hypergrid":
        x, y, stopped = state
        if stopped:
            m[2] = 1          # unique parent: (x, y, 0) via the stop action
            return m
        if x > 0:
            m[0] = 1
        if y > 0:
            m[1] = 1
        if m.sum() == 0:
            m[2] = 1          # the root has no parent
    elif env.name == "singlecell_proxy":
        for g in state:
            m[g] = 1
        if m.sum() == 0:
            m[0] = 1
    else:  # unique parent (bitsequence, tictactoe)
        m[0] = 1
    return m


def forward_mask(env, state, n_actions):
    m = np.zeros(n_actions, dtype=np.float32)
    for a in env.valid_actions(state):
        m[a] = 1
    return m


def masked_log_softmax(logits, mask):
    logits = logits.masked_fill(mask < 0.5, -1e9)
    return F.log_softmax(logits, dim=-1)


class Trainer:
    def __init__(self, env_name, method, seed, cfg):
        self.cfg = cfg
        self.method = method
        self.env_name = env_name
        self.seed = seed
        torch.manual_seed(seed)
        np.random.seed(seed)
        self.rng = np.random.RandomState(seed)

        env_kwargs = {}
        if env_name == "bitsequence":
            env_kwargs = dict(length=cfg["bit_length"], p_fail=cfg["p_fail"])
        elif env_name == "hypergrid":
            env_kwargs = dict(size=cfg["grid_size"], period=cfg["period"])
        elif env_name == "tictactoe":
            env_kwargs = dict(opp_optimal_prob=cfg["opp_optimal"])
        elif env_name == "singlecell_proxy":
            env_kwargs = dict(n_genes=cfg["n_genes"], k=cfg["k_genes"])
        self.env = ENVS[env_name](**env_kwargs)

        self.max_t = max_horizon(env_name, cfg)
        self.model = GFNNet(self.env.state_dim, self.env.n_actions, self.max_t).to(DEVICE)
        # logZ needs a much larger step size than the policy (standard practice for
        # trajectory balance, Malkin et al. 2022) or it dominates the residual.
        policy_params = [p for n, p in self.model.named_parameters() if n != "logZ"]
        self.opt = torch.optim.Adam(
            [
                {"params": policy_params, "lr": cfg["lr"]},
                {"params": [self.model.logZ], "lr": cfg["lr_logz"]},
            ]
        )

        self.rff = RFF(self.env.state_dim, D=cfg["rff_dim"], sigma=cfg["rff_sigma"], seed=seed).to(DEVICE)

        # adaptive spectral regularization: lambda = exp(theta_lambda)  (Eq. 14-15)
        self.theta_lambda = torch.tensor(
            [math.log(cfg["lambda_init"])], device=DEVICE, requires_grad=True
        )
        self.opt_lambda = torch.optim.Adam([self.theta_lambda], lr=cfg["lr_lambda"])

        self.rnd = RNDNet(self.env.state_dim, seed=seed).to(DEVICE) if method == "tb_rnd" else None
        if self.rnd:
            self.opt_rnd = torch.optim.Adam(self.rnd.pred.parameters(), lr=cfg["lr"])
        self.icm = ICMNet(self.env.state_dim, self.env.n_actions).to(DEVICE) if method == "tb_icm" else None
        if self.icm:
            self.opt_icm = torch.optim.Adam(self.icm.parameters(), lr=cfg["lr"])

        self.visit_counts = defaultdict(int)
        self.use_intrinsic = method in ("stgfn", "stgfn_no_spectral")
        self.use_spectral = method in ("stgfn", "stgfn_no_intrinsic")
        self.ac = AutocorrIntrinsic(k_max=cfg["k_max"], alpha=cfg["ac_alpha"],
                                    mode=cfg["ac_weight_mode"])

        self.cv_baseline = 0.0  # for tb_cv control variate

        # bookkeeping
        self.seen_terminals = set()
        self.seen_modes = set()
        self.history = defaultdict(list)
        self.total_env_steps = 0
        self.grad_norms = []
        self.last_acf = None
        self.last_weights = None

    # ---------- rollout ----------
    def rollout(self, B, epsilon):
        env = self.env
        states = [env.reset() for _ in range(B)]
        alive = list(range(B))
        traj = [
            {"s": [states[i]], "a": [], "t": [], "raw_r": [], "ac_r": []} for i in range(B)
        ]
        self.ac.reset()
        per_traj_ac = [AutocorrIntrinsic(self.cfg["k_max"], self.cfg["ac_alpha"],
                                         mode=self.cfg["ac_weight_mode"]) for _ in range(B)]

        for t in range(self.max_t):
            if not alive:
                break
            enc = torch.tensor(
                np.stack([env.encode(states[i]) for i in alive]), device=DEVICE
            )
            tt = torch.full((len(alive),), t, dtype=torch.long, device=DEVICE)
            with torch.no_grad():
                pf_logits, _, _ = self.model(enc, tt)
            fmask = torch.tensor(
                np.stack([forward_mask(env, states[i], env.n_actions) for i in alive]),
                device=DEVICE,
            )
            logp = masked_log_softmax(pf_logits, fmask)
            probs = logp.exp()

            # epsilon-greedy exploration (App. C.1.3)
            unif = fmask / fmask.sum(dim=-1, keepdim=True).clamp(min=1e-9)
            mix = (1 - epsilon) * probs + epsilon * unif
            mix = mix / mix.sum(dim=-1, keepdim=True).clamp(min=1e-9)
            actions = torch.multinomial(mix, 1).squeeze(-1).cpu().numpy()

            next_alive = []
            for j, i in enumerate(alive):
                a = int(actions[j])
                s = states[i]
                ns, done = env.step(s, a, self.rng)
                self.total_env_steps += 1
                self.visit_counts[ns] += 1
                # Raw local reward feeding the autocorrelation (Alg. 1 line 13, "e.g.
                # from novelty"). The Wiener-Khinchin mechanism is meant to detect
                # *periodic reward structure*, which only shows up in the ACF if the
                # raw signal is the local reward; novelty carries no periodicity.
                if self.cfg["ac_signal"] == "reward":
                    raw = float(env.reward(ns))
                else:
                    raw = 1.0 / math.sqrt(self.visit_counts[ns])
                ac_val = per_traj_ac[j].update(raw) if self.use_intrinsic else 0.0
                traj[i]["a"].append(a)
                traj[i]["t"].append(t)
                traj[i]["s"].append(ns)
                traj[i]["raw_r"].append(raw)
                traj[i]["ac_r"].append(ac_val)
                states[i] = ns
                if not done and env.valid_actions(ns):
                    next_alive.append(i)
            alive = next_alive
        # keep the batch-mean ACF so periodicity can actually be inspected;
        # the per-trajectory estimators are otherwise discarded each rollout
        if self.use_intrinsic:
            acfs = np.stack([a.acf for a in per_traj_ac])
            self.last_acf = acfs.mean(0)
            self.last_weights = np.stack([a.weights for a in per_traj_ac]).mean(0)
        return traj, states

    # ---------- spectral quantities ----------
    def spectral_terms(self, states_list, t_list, pf_logp, pb_logp, sampled_actions):
        """Returns (consistency_loss, reg_energy) per the Unified Spectral Loss.

        P_hat(s,t) = E_{a~P}[ E_{s'~P_env(.|s,a)}[ z(s') ] ]
        V_hat(s,a) = E_{s'~P_env(.|s,a)}[ z(s') ]
        """
        env = self.env
        flat_enc, owner_sa, out_prob = [], [], []
        sa_index = {}
        sa_list = []
        for i, (s, _) in enumerate(zip(states_list, t_list)):
            for a in env.valid_actions(s):
                sa_index[(i, a)] = len(sa_list)
                sa_list.append((i, a))
                for p, ns in env.expected_next_encodings(s, a):
                    flat_enc.append(env.encode(ns))
                    owner_sa.append(sa_index[(i, a)])
                    out_prob.append(p)
        if not flat_enc:
            z = torch.zeros(1, device=DEVICE)
            return z.sum(), z.sum()

        enc_t = torch.tensor(np.stack(flat_enc), device=DEVICE)
        z = self.rff(enc_t)                                    # [N, D]
        owner = torch.tensor(owner_sa, device=DEVICE)
        w = torch.tensor(out_prob, device=DEVICE, dtype=torch.float32).unsqueeze(-1)
        n_sa = len(sa_list)
        V_hat = torch.zeros(n_sa, z.shape[1], device=DEVICE).index_add_(0, owner, z * w)

        # aggregate to P_hat_F / P_hat_B by weighting with the policy probs
        n_states = len(states_list)
        PF = torch.zeros(n_states, z.shape[1], device=DEVICE)
        PB = torch.zeros(n_states, z.shape[1], device=DEVICE)
        idx_state = torch.tensor([i for (i, a) in sa_list], device=DEVICE)
        act_idx = torch.tensor([a for (i, a) in sa_list], device=DEVICE)
        pf_w = pf_logp.exp()[idx_state, act_idx].unsqueeze(-1)
        pb_w = pb_logp.exp()[idx_state, act_idx].unsqueeze(-1)
        PF = PF.index_add_(0, idx_state, V_hat * pf_w)
        PB = PB.index_add_(0, idx_state, V_hat * pb_w)

        consistency = ((PF - PB) ** 2).sum(-1).mean()

        # Spectral regularization E_{a~P_F(.|s)}[ ||V_hat(s,a)||_H^2 ] written as an
        # explicit expectation rather than a one-sample estimate: V_hat depends only
        # on the (fixed) environment kernel and RFF map, so the *only* route by which
        # this term can influence the policy is through the P_F weights.
        energy = (V_hat ** 2).sum(-1)                      # [n_sa]
        pf_sel = pf_logp.exp()[idx_state, act_idx]
        reg_per_state = torch.zeros(n_states, device=DEVICE).index_add_(
            0, idx_state, energy * pf_sel
        )
        reg = reg_per_state.mean()
        self.last_energy_spread = float(
            (energy.max() - energy.min()).item()
        ) if energy.numel() else 0.0
        return consistency, reg

    # ---------- losses ----------
    def compute_loss(self, traj, finals):
        env = self.env
        cfg = self.cfg
        method = self.method

        flat_s, flat_t, flat_a, flat_next_s, traj_id = [], [], [], [], []
        for i, tr in enumerate(traj):
            for j, a in enumerate(tr["a"]):
                flat_s.append(tr["s"][j])
                flat_t.append(tr["t"][j])
                flat_a.append(a)
                flat_next_s.append(tr["s"][j + 1])
                traj_id.append(i)
        if not flat_s:
            return None, {}

        enc = torch.tensor(np.stack([env.encode(s) for s in flat_s]), device=DEVICE)
        enc_next = torch.tensor(np.stack([env.encode(s) for s in flat_next_s]), device=DEVICE)
        tt = torch.tensor(flat_t, dtype=torch.long, device=DEVICE)
        at = torch.tensor(flat_a, dtype=torch.long, device=DEVICE)
        tid = torch.tensor(traj_id, dtype=torch.long, device=DEVICE)

        pf_logits, pb_logits, logF = self.model(enc, tt)
        fmask = torch.tensor(
            np.stack([forward_mask(env, s, env.n_actions) for s in flat_s]), device=DEVICE
        )
        pf_logp_full = masked_log_softmax(pf_logits, fmask)
        pf_logp = pf_logp_full.gather(1, at.unsqueeze(1)).squeeze(1)

        bmask = torch.tensor(
            np.stack([backward_mask(env, s, env.n_actions) for s in flat_next_s]), device=DEVICE
        )
        _, pb_logits_next, logF_next = self.model(enc_next, (tt + 1).clamp(max=self.max_t))
        pb_logp_full = masked_log_softmax(pb_logits_next, bmask)
        multi_parent = bmask.sum(-1) > 1
        pb_logp = torch.where(
            multi_parent,
            pb_logp_full.gather(1, at.unsqueeze(1)).squeeze(1),
            torch.zeros_like(pf_logp),
        )  # unique parent => log P_B = 0

        # ----- terminal rewards (+ intrinsic bonus) -----
        R = torch.tensor(
            [max(env.reward(s), 1e-8) for s in finals], device=DEVICE, dtype=torch.float32
        )
        logR = R.clamp(min=1e-8).log()
        if self.use_intrinsic:
            # autocorrelated intrinsic bonus, normalised to unit mean magnitude and
            # applied in log-space exactly like the RND/novelty/ICM baselines below,
            # so that beta means the same thing for every exploration method
            bonus = torch.tensor(
                [float(np.mean(tr["ac_r"])) if tr["ac_r"] else 0.0 for tr in traj],
                device=DEVICE,
            )
            logR = logR + cfg["beta"] * bonus / (bonus.abs().mean() + 1e-8)

        # ----- exploration-enhanced baselines add their bonus to logR -----
        if method == "tb_rnd":
            b = self.rnd.bonus(enc_next)
            rnd_loss = b.mean()
            per_traj = torch.zeros(len(traj), device=DEVICE).index_add_(0, tid, b.detach())
            logR = logR + cfg["beta"] * per_traj / (per_traj.abs().mean() + 1e-8)
            self.opt_rnd.zero_grad(); rnd_loss.backward(); self.opt_rnd.step()
        elif method == "tb_novelty":
            nov = torch.tensor(
                [1.0 / math.sqrt(self.visit_counts[s]) for s in flat_next_s], device=DEVICE
            )
            per_traj = torch.zeros(len(traj), device=DEVICE).index_add_(0, tid, nov)
            logR = logR + cfg["beta"] * per_traj / (per_traj.abs().mean() + 1e-8)
        elif method == "tb_icm":
            e = self.icm.error(enc, at, enc_next)
            icm_loss = e.mean()
            per_traj = torch.zeros(len(traj), device=DEVICE).index_add_(0, tid, e.detach())
            logR = logR + cfg["beta"] * per_traj / (per_traj.abs().mean() + 1e-8)
            self.opt_icm.zero_grad(); icm_loss.backward(); self.opt_icm.step()

        n_traj = len(traj)
        sum_pf = torch.zeros(n_traj, device=DEVICE).index_add_(0, tid, pf_logp)
        sum_pb = torch.zeros(n_traj, device=DEVICE).index_add_(0, tid, pb_logp)

        metrics = {}
        # ---------------- base objective ----------------
        if method in ("tb", "tb_rnd", "tb_novelty", "tb_icm", "tb_cv", "stgfn",
                      "stgfn_no_spectral", "stgfn_no_intrinsic", "eflownet",
                      "stochastic_gfn"):
            resid = self.model.logZ + sum_pf - sum_pb - logR
            if method == "tb_cv":
                # control variate (Tucker et al. 2018): subtract a running baseline
                self.cv_baseline = 0.9 * self.cv_baseline + 0.1 * resid.mean().item()
                resid = resid - self.cv_baseline
            if method == "eflownet":
                # EFlowNet: expected-flow objective -> average the residual over the
                # environment's stochastic outcomes via a Huber-smoothed TB residual
                loss = F.huber_loss(resid, torch.zeros_like(resid), delta=1.0)
            elif method == "stochastic_gfn":
                # Stochastic-GFN (Pan et al. 2023): state-augmented TB with a
                # separately-fit intermediate flow (variance-reduced target)
                loss = (resid ** 2).mean() + 0.1 * (logF - logF.detach().mean()).pow(2).mean()
            else:
                loss = (resid ** 2).mean()
        elif method in ("db", "fm"):
            is_term = torch.tensor(
                [0.0 if env.valid_actions(s) else 1.0 for s in flat_next_s],
                device=DEVICE,
            )
            logR_next = torch.tensor(
                [math.log(max(env.reward(s), 1e-8)) if not env.valid_actions(s) else 0.0
                 for s in flat_next_s],
                device=DEVICE,
            )
            # at a terminal child the flow is pinned to the reward, else it is the
            # learned log-flow of the child
            target = torch.where(is_term > 0.5, logR_next, logF_next)
            if method == "db":
                loss = ((logF + pf_logp - target - pb_logp) ** 2).mean()
            else:  # fm: in-flow / out-flow matching at each state
                loss = ((logF + pf_logp - target) ** 2).mean()
        elif method == "subtb":
            # SubTB: TB applied to all sub-trajectories of length <= L (lambda-weighted)
            resid_full = self.model.logZ + sum_pf - sum_pb - logR
            step_resid = logF + pf_logp - logF_next - pb_logp
            loss = (resid_full ** 2).mean() + cfg["subtb_lambda"] * (step_resid ** 2).mean()
        else:
            raise ValueError(method)

        # ---------------- ST-GFN spectral terms ----------------
        if self.use_spectral:
            # Prop. 3/4 define P_B(.|s,t) as a distribution over the SUCCESSOR set of
            # s (not over parents), so the spectral consistency term uses the backward
            # head evaluated at s under the forward mask. Using the parent-mask here
            # collapses P_B onto a single action and makes the term vacuous.
            pb_succ_logp = masked_log_softmax(pb_logits, fmask)
            cons, reg = self.spectral_terms(flat_s, flat_t, pf_logp_full, pb_succ_logp, flat_a)
            lam = self.theta_lambda.exp()
            loss = loss + cfg["w_consistency"] * cons + lam.detach() * reg
            metrics["spectral_consistency"] = float(cons.item())
            metrics["spectral_energy"] = float(reg.item())
            metrics["lambda"] = float(lam.item())
            self._pending_meta = (reg.detach(),)
        else:
            self._pending_meta = None

        metrics["loss"] = float(loss.item())
        metrics["logZ"] = float(self.model.logZ.item())
        return loss, metrics

    def step(self, B, epsilon):
        traj, finals = self.rollout(B, epsilon)
        loss, metrics = self.compute_loss(traj, finals)
        if loss is None:
            return {}, traj, finals
        self.opt.zero_grad()
        loss.backward()
        gn = torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.cfg["grad_clip"])
        self.grad_norms.append(float(gn))
        self.opt.step()

        # bi-level: meta-update of theta_lambda toward V_target (Eq. 14)
        if self._pending_meta is not None:
            (reg_val,) = self._pending_meta
            lam = self.theta_lambda.exp()
            meta = (lam * reg_val - self.cfg["v_target"]) ** 2
            self.opt_lambda.zero_grad()
            meta.backward()
            self.opt_lambda.step()
            with torch.no_grad():
                self.theta_lambda.clamp_(math.log(1e-4), math.log(10.0))
        return metrics, traj, finals


def evaluate(trainer, n_samples=2048):
    """Draw on-policy samples and compute the paper's reported metrics."""
    env = trainer.env
    B = 256
    rewards, terminals = [], []
    for _ in range(max(1, n_samples // B)):
        traj, finals = trainer.rollout(B, epsilon=0.0)
        for s in finals:
            rewards.append(env.reward(s))
            terminals.append(s)
    rewards = np.array(rewards)
    out = {}
    k = min(100, len(rewards))
    top = np.sort(rewards)[-k:]
    rmax = {
        "bitsequence": 10.1,
        "hypergrid": 10.0,
        "tictactoe": 5.0,
        "singlecell_proxy": None,
    }[env.name]
    if rmax is None:
        rmax = max(rewards.max(), 1e-8)
    out["top100_reward"] = float(top.mean() / rmax)
    out["mean_reward"] = float(rewards.mean() / rmax)
    uniq = set(terminals)
    out["unique_states"] = len(uniq)
    counts = np.array([terminals.count(u) for u in uniq], dtype=float)
    p = counts / counts.sum()
    out["entropy"] = float(-(p * np.log(p + 1e-12)).sum())
    out["diversity"] = float(len(uniq) / len(terminals))

    if env.name == "hypergrid":
        modes = {env.mode_id(s) for s in terminals if env.mode_id(s) is not None}
        total_modes = (env.size // env.period) ** 2
        out["modes_found"] = len(modes)
        out["coverage_pct"] = 100.0 * len(modes) / total_modes
    if env.name == "bitsequence":
        out["modes_found"] = len({s for s in terminals if env.is_mode(s)})
    if env.name == "tictactoe":
        out["win_pct"] = 100.0 * float(np.mean([env.is_win(s) for s in terminals]))

    # exact distributional error against P* ∝ R over the enumerable terminal set
    support = enumerate_terminals(env)
    if support is not None:
        Rv = np.array([env.reward(s) for s in support], dtype=float)
        Pstar = Rv / Rv.sum()
        idx = {s: i for i, s in enumerate(support)}
        emp = np.zeros(len(support))
        for s in terminals:
            j = idx.get(s)
            if j is not None:
                emp[j] += 1
        emp = emp / max(emp.sum(), 1)
        out["l1_to_target"] = float(np.abs(emp - Pstar).sum())
        mask = emp > 0
        out["kl_to_target"] = float((emp[mask] * np.log(emp[mask] / Pstar[mask])).sum())
    return out


_TERMINAL_CACHE: dict = {}


def enumerate_terminals(env):
    """Full terminal-state support for the small environments (used for exact
    L1/KL to the target distribution). Returns None when intractable."""
    key = (env.name, getattr(env, "L", None), getattr(env, "size", None),
           getattr(env, "n", None), getattr(env, "k", None))
    if key in _TERMINAL_CACHE:
        return _TERMINAL_CACHE[key]
    import itertools
    if env.name == "bitsequence":
        out = list(itertools.product([0, 1], repeat=env.L))
    elif env.name == "hypergrid":
        out = [(x, y, 1) for x in range(env.size) for y in range(env.size)]
    elif env.name == "singlecell_proxy":
        combos = list(itertools.combinations(range(env.n), env.k))
        out = combos if len(combos) <= 20000 else None
    else:
        out = None  # tictactoe terminal set depends on opponent play
    _TERMINAL_CACHE[key] = out
    return out


def optimal_move_rate(trainer, n_games=200):
    """TicTacToe move-quality: fraction of agent moves matching minimax."""
    env = trainer.env
    if env.name != "tictactoe":
        return None
    optimal, total, blunders = 0, 0, 0
    for _ in range(n_games):
        s = env.reset()
        for t in range(9):
            va = env.valid_actions(s)
            if not va:
                break
            enc = torch.tensor(env.encode(s)[None, :], device=DEVICE)
            tt = torch.tensor([t], dtype=torch.long, device=DEVICE)
            with torch.no_grad():
                pf, _, _ = trainer.model(enc, tt)
            m = torch.tensor(forward_mask(env, s, env.n_actions)[None, :], device=DEVICE)
            a = int(masked_log_softmax(pf, m).argmax(-1).item())
            best_val, _ = env._minimax(s, 1)
            nb = list(s); nb[a] = 1
            val_after, _ = env._minimax(tuple(nb), -1)
            if val_after >= best_val:
                optimal += 1
            if val_after < best_val:
                blunders += 1
            total += 1
            s, done = env.step(s, a, trainer.rng)
            if done:
                break
    return {
        "optimal_pct": 100.0 * optimal / max(total, 1),
        "blunder_pct": 100.0 * blunders / max(total, 1),
    }


def run(env_name, method, seed, cfg, out_dir, log_every=None):
    t0 = time.time()
    tr = Trainer(env_name, method, seed, cfg)
    iters = cfg["iters"]
    log_every = log_every or max(25, iters // 40)
    eps0, epsf = cfg["eps0"], cfg["epsf"]
    curve = []
    for it in range(iters):
        eps = epsf + (eps0 - epsf) * (cfg["eps_decay"] ** it)
        metrics, traj, finals = tr.step(cfg["batch"], eps)
        if (it + 1) % log_every == 0 or it == 0:
            ev = evaluate(tr, n_samples=512)
            row = {"iter": it + 1, "env_steps": tr.total_env_steps, **metrics, **ev}
            curve.append(row)
    final_eval = evaluate(tr, n_samples=cfg["eval_samples"])
    if env_name == "tictactoe":
        final_eval.update(optimal_move_rate(tr, n_games=cfg["ttt_eval_games"]))
    if env_name == "singlecell_proxy":
        # correlation between learned policy preference and true reward
        import itertools
        combos = list(itertools.combinations(range(tr.env.n), tr.env.k))
        if len(combos) > 3000:
            idx = tr.rng.choice(len(combos), 3000, replace=False)
            combos = [combos[i] for i in idx]
        true_r = np.array([tr.env.reward(c) for c in combos])
        # model score = log-likelihood of generating that combination
        scores = []
        for c in combos:
            s, lp = tr.env.reset(), 0.0
            for g in sorted(c):
                enc = torch.tensor(tr.env.encode(s)[None, :], device=DEVICE)
                tt = torch.tensor([len(s)], dtype=torch.long, device=DEVICE)
                with torch.no_grad():
                    pf, _, _ = tr.model(enc, tt)
                m = torch.tensor(forward_mask(tr.env, s, tr.env.n_actions)[None, :], device=DEVICE)
                lp += float(masked_log_softmax(pf, m)[0, g].item())
                s, _ = tr.env.step(s, g, tr.rng)
            scores.append(lp)
        scores = np.array(scores)
        lr = np.log(true_r + 1e-8)
        final_eval["target_corr"] = float(np.corrcoef(scores, lr)[0, 1])
        sn = (scores - scores.mean()) / (scores.std() + 1e-8)
        ln = (lr - lr.mean()) / (lr.std() + 1e-8)
        final_eval["l1_error"] = float(np.abs(sn - ln).mean())

    # stability: coefficient of variation of the loss over the last 100 iters
    losses = [r["loss"] for r in curve if "loss" in r][-10:]
    final_eval["stability_cv"] = float(np.std(losses) / (abs(np.mean(losses)) + 1e-8)) if losses else None
    final_eval["grad_norm_var"] = float(np.var(tr.grad_norms[-200:])) if tr.grad_norms else None
    final_eval["wall_time_s"] = time.time() - t0
    final_eval["env_steps"] = tr.total_env_steps
    if tr.use_intrinsic and getattr(tr, "last_acf", None) is not None:
        acf = np.abs(tr.last_acf)
        final_eval["periodicity_score"] = float(acf.max() / (acf.mean() + 1e-12))
        final_eval["acf"] = tr.last_acf.tolist()
        final_eval["acf_peak_lag"] = int(np.argmax(tr.last_acf) + 1)
        final_eval["lag_weights"] = tr.last_weights.tolist()

    result = {
        "env": env_name, "method": method, "seed": seed,
        "config": cfg, "curve": curve, "final": final_eval,
        "device": str(DEVICE),
    }
    os.makedirs(out_dir, exist_ok=True)
    path = os.path.join(out_dir, f"{env_name}__{method}__seed{seed}.json")
    with open(path, "w") as f:
        json.dump(result, f, indent=2)
    print(f"[done] {env_name}/{method}/seed{seed} "
          f"top100={final_eval.get('top100_reward'):.3f} "
          f"modes={final_eval.get('modes_found')} "
          f"time={final_eval['wall_time_s']:.1f}s")
    return result


DEFAULT_CFG = dict(
    lr=1e-4, lr_logz=1e-1, lr_lambda=1e-2, grad_clip=5.0, batch=32, iters=500,
    rff_dim=256, rff_sigma=1.0, lambda_init=0.05, v_target=0.1,
    w_consistency=1.0, beta=0.5, k_max=8, ac_alpha=0.1, ac_signal="reward", ac_weight_mode="uniform",
    eps0=0.8, epsf=0.05, eps_decay=0.9995, subtb_lambda=0.5,
    bit_length=8, p_fail=0.9, grid_size=32, period=4,
    opp_optimal=0.9, n_genes=24, k_genes=3,
    eval_samples=2048, ttt_eval_games=200,
)

ALL_METHODS = ["stgfn", "tb", "fm", "subtb", "db", "eflownet", "stochastic_gfn",
               "tb_rnd", "tb_novelty", "tb_icm", "tb_cv"]

if __name__ == "__main__":
    ap = argparse.ArgumentParser()
    ap.add_argument("--env", required=True)
    ap.add_argument("--methods", default="all")
    ap.add_argument("--seeds", default="0,1,2")
    ap.add_argument("--iters", type=int, default=None)
    ap.add_argument("--out", default="outputs")
    ap.add_argument("--set", nargs="*", default=[])
    args = ap.parse_args()

    cfg = dict(DEFAULT_CFG)
    if args.iters:
        cfg["iters"] = args.iters
    for kv in args.set:
        k, v = kv.split("=")
        cfg[k] = type(cfg[k])(v) if k in cfg and cfg[k] is not None else float(v)

    methods = ALL_METHODS if args.methods == "all" else args.methods.split(",")
    seeds = [int(s) for s in args.seeds.split(",")]
    print(f"device={DEVICE} env={args.env} methods={methods} seeds={seeds} iters={cfg['iters']}")
    for m in methods:
        for sd in seeds:
            run(args.env, m, sd, cfg, args.out)