sts2-bot/run.py

#!/usr/bin/env python3
"""
run.py -- the observe -> decide -> act loop.

The loop is strictly closed. Playing a card removes it from hand and shifts
every later index, so we re-read the state after every single action. We never
precompute an action list.

usage:
    python3 run.py --dry-run --steps 5     # show decisions, touch nothing
    python3 run.py --steps 40              # actually play
    python3 run.py --steps 40 --no-jev     # heuristics only
"""

from __future__ import annotations

import argparse
import json
import pathlib
import sys
import time

import brain
import facts as F
import sts2
from jev import JevClient, JevError

DECK_FILE = pathlib.Path("deck.json")

# Known starting decks, used only until the first combat exposes the real one.
# Source: the character_select state, which lists starting_deck per character.
STARTING_DECKS: dict[str, dict[str, int]] = {
    "The Ironclad": {"Strike": 5, "Defend": 4, "Bash": 1},
    "The Silent": {"Strike": 5, "Defend": 5, "Neutralize": 1, "Survivor": 1},
    "The Regent": {"Strike": 4, "Defend": 4, "Falling Star": 1, "Venerate": 1},
}


def load_deck() -> dict | None:
    if DECK_FILE.exists():
        try:
            return json.loads(DECK_FILE.read_text())
        except (json.JSONDecodeError, OSError):
            return None
    return None


def save_deck(deck: dict) -> None:
    DECK_FILE.write_text(json.dumps(deck, indent=2, sort_keys=True))


def observe() -> dict:
    return sts2.state()


def preflight(obs: dict) -> str | None:
    """
    Detect states the bot cannot proceed from, so a session does not silently
    burn its whole step budget doing nothing.

    The one that actually bit: after a run ends with a pending Timeline epoch,
    the main menu offers only `settings` and `quit`, and the mod REFUSES to
    automate the reveal. A whole A/B arm ran with 0 decisions before this was
    noticed.
    """
    if obs.get("state_type") != "menu" or obs.get("menu_screen") != "main":
        return None

    options = obs.get("options") or []
    names = {o if isinstance(o, str) else o.get("name") for o in options}
    if names & {"singleplayer", "continue"}:
        return None

    blocked = obs.get("blocked_options") or []
    for entry in blocked:
        if isinstance(entry, dict) and entry.get("reason") == "manual_epoch_reveal_required":
            pending = ", ".join(entry.get("pending_epoch_ids") or [])
            return (
                "BLOCKED: the Timeline has unrevealed epochs (" + pending + ").\n"
                "The mod refuses to automate this by design, and the main menu\n"
                "offers only settings/quit until it is done.\n"
                "  -> Open the Timeline IN GAME and reveal the epoch by hand,\n"
                "     then re-run."
            )

    return f"BLOCKED: main menu offers only {sorted(n for n in names if n)}; cannot start a run."


def main() -> int:
    ap = argparse.ArgumentParser()
    ap.add_argument("--steps", type=int, default=20)
    ap.add_argument("--dry-run", action="store_true")
    ap.add_argument("--no-jev", action="store_true")
    ap.add_argument("--pause", type=float, default=0.6,
                    help="seconds to wait after each action")
    ap.add_argument("--stuck-seconds", type=float, default=25.0,
                    help="give up if the state does not change for this long")
    ap.add_argument("--max-duplicate-waits", type=int, default=3,
                    help="how many times to suppress an identical repeated action "
                         "on an unchanged state before trying it again")
    ap.add_argument("--capture-dir", default="capture")
    ap.add_argument("--card-skip-policy", choices=("jev", "combined"), default=None,
                    help="how card-reward skips are decided (default: brain's own)")
    args = ap.parse_args()

    if not sts2.is_up():
        print(f"game not reachable at {sts2.BASE}")
        return 1

    # Fail fast on a state the bot cannot leave, instead of burning the whole
    # step budget on rejected actions.
    try:
        blocker = preflight(observe())
    except sts2.Sts2Error as exc:
        print(f"cannot read state: {exc}")
        return 1
    if blocker:
        print(blocker)
        return 2

    if args.card_skip_policy:
        brain.CARD_SKIP_POLICY = args.card_skip_policy
    print(f"card skip policy: {brain.CARD_SKIP_POLICY}")

    client = None
    if not args.no_jev:
        try:
            client = JevClient()
            print(f"jev ready: {client!r}")
        except JevError as exc:
            print(f"jev unavailable, using heuristics only: {exc}")

    capdir = pathlib.Path(args.capture_dir)
    capdir.mkdir(exist_ok=True)
    trace_path = capdir / "decisions.jsonl"

    def trace(record: dict) -> None:
        # One JSON line per decided action. Tail it while the bot plays:
        #   tail -f capture/decisions.jsonl | jq
        record["ts"] = time.strftime("%H:%M:%S")
        with trace_path.open("a", encoding="utf-8") as fh:
            fh.write(json.dumps(record, sort_keys=True, default=str) + "\n")

    stats = {"code": 0, "jev": 0, "fallback": 0}
    jev_calls = 0
    jev_tokens = 0
    started = time.monotonic()

    # The card_reward state does not expose the deck, but combat states expose
    # all four piles. Snapshot composition during combat and persist it so it
    # survives a restart.
    deck_snapshot: dict | None = load_deck()
    if deck_snapshot:
        print(f"deck snapshot loaded: {deck_snapshot}")
    waits = 0
    rejected = 0
    last_sig: str | None = None
    same_state = 0
    same_state_since = time.monotonic()
    last_ok = True
    last_action_key: tuple | None = None
    duplicate_waits = 0

    for step in range(1, args.steps + 1):
        try:
            obs = observe()
        except sts2.Sts2Error as exc:
            print(f"[{step:03d}] state read failed: {str(exc)[:160]}")
            return 1

        st = obs.get("state_type")

        # Guard against re-acting while the game is still animating a transition.
        # An identical state after our own action means the action has not landed
        # yet; acting again queues duplicates (e.g. three map moves in a row).
        sig = json.dumps(obs, sort_keys=True)
        if sig == last_sig:
            same_state += 1
        else:
            same_state = 0
            last_sig = sig
            same_state_since = time.monotonic()

        # Time-based, not count-based: a boss death animation plus the rewards
        # transition can easily exceed a fixed number of reads.
        unchanged_for = time.monotonic() - same_state_since
        if unchanged_for > args.stuck_seconds:
            print(f"[{step:03d}] STUCK: state unchanged for {unchanged_for:.0f}s -- stopping")
            print(json.dumps(obs, indent=2)[:900])
            break

        # Only wait when our own action actually landed and the game is still
        # animating. If the action was rejected, fall through and pick a
        # different one instead of waiting out the stuck counter.
        #
        # NOTE: this check now happens AFTER deciding, and only suppresses a
        # REPEATED action. Waiting on "state unchanged" alone blocked
        # legitimate sequences -- character select needs select-then-embark, and
        # the screen does not change between them, so the bot stalled forever.

        if st in ("monster", "elite", "boss"):
            f = F.combat_facts(obs)
            (capdir / f"live_{step:03d}_combat.json").write_text(json.dumps(obs, indent=2))
            print(f"[{step:03d}] COMBAT  {f.describe().splitlines()[0]}")
            for line in f.describe().splitlines()[1:]:
                print(f"        {line}")
            if f.deck_counts and f.deck_counts != deck_snapshot:
                deck_snapshot = f.deck_counts
                save_deck(deck_snapshot)

        decision = None
        decide_error = None
        try:
            decision = brain.decide(obs, client, deck_snapshot)
        except JevError as exc:
            print(f"[{step:03d}] jev error: {str(exc)[:160]}")
            decide_error = f"JevError: {str(exc)[:160]}"
            decision = brain.simple_decision(obs, client, deck_snapshot)
        except Exception as exc:  # noqa: BLE001
            # A network blip or an unexpected shape must not end the run. Fall
            # back to the deterministic handlers and keep playing.
            print(f"[{step:03d}] unexpected error in decide(): "
                  f"{type(exc).__name__}: {str(exc)[:160]}")
            decide_error = f"{type(exc).__name__}: {str(exc)[:160]}"
            try:
                decision = brain.simple_decision(obs, client, deck_snapshot)
            except Exception as inner:  # noqa: BLE001
                print(f"[{step:03d}] fallback also failed: {inner}")
                decision = None

        if decision is None:
            trace({"step": step, "state_type": st,
                   "event": "no_decision", "error": decide_error})
            print(f"[{step:03d}] no decision for state_type={st!r} -- stopping")
            print(json.dumps(obs, indent=2)[:800])
            break

        # Between turns there is nothing to do but look again.
        if decision.action == "__wait__":
            waits += 1
            print(f"[{step:03d}] WAIT    {decision.reason}")
            time.sleep(args.pause)
            continue

        # Suppress DUPLICATE actions on an unchanged state, but only for a
        # bounded number of reads. Proposing a DIFFERENT action is always
        # allowed, which is what makes select-then-embark and multi-purchase
        # shops work. The bound matters because some actions legitimately need
        # repeating (multi-line Ancient dialogue) and some transitions are just
        # slow -- waiting forever on those stalls the run.
        action_key = (decision.action, json.dumps(decision.params, sort_keys=True))
        if same_state == 0:
            duplicate_waits = 0

        if same_state > 0 and last_ok and action_key == last_action_key:
            duplicate_waits += 1
            if duplicate_waits <= args.max_duplicate_waits:
                waits += 1
                print(f"[{step:03d}] WAIT    same action on unchanged state ({same_state})")
                time.sleep(args.pause)
                continue
            print(f"[{step:03d}] RETRY   repeating {decision.action} after "
                  f"{duplicate_waits} waits on an unchanged state")
            duplicate_waits = 0

        stats[decision.source] = stats.get(decision.source, 0) + 1
        print(f"[{step:03d}] DECIDE  {decision}")
        trace({
            "step": step,
            "state_type": st,
            "run": obs.get("run"),
            "event": "decide",
            "source": decision.source,
            "action": decision.action,
            "params": decision.params,
            "reason": decision.reason,
            "confidence": decision.confidence,
            "error": decide_error,
        })
        last_action_key = action_key

        if args.dry_run:
            continue

        try:
            result = sts2.act(decision.action, **decision.params)
        except sts2.Sts2Error as exc:
            print(f"[{step:03d}] action failed: {str(exc)[:200]}")
            trace({"step": step, "event": "action_error",
                   "action": decision.action, "error": str(exc)[:200]})
            break

        if not result.ok:
            print(f"[{step:03d}] action rejected: {result.message}")
            trace({"step": step, "event": "action_rejected",
                   "action": decision.action, "message": result.message})
            last_ok = False
            rejected += 1
            if rejected >= 6:
                print(f"[{step:03d}] STUCK: {rejected} consecutive rejections -- stopping")
                print(json.dumps(obs, indent=2)[:900])
                break
            # Transient rejections while the game animates are normal -- a
            # rest-site `proceed` right after a heal is rejected for a moment
            # and then succeeds. Back off longer than the usual pause.
            time.sleep(args.pause * 3)
            continue
        else:
            rejected = 0
            last_ok = True

        time.sleep(args.pause)

    elapsed = time.monotonic() - started
    print()
    print(f"steps={step} waits={waits} elapsed={elapsed:.1f}s  sources={stats}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())