"""Closed-loop game simulations driven by any `decide(state, questions) -> answers` callable (the System One call). The method of a Snake race: the game code works out what each move would do and words it; the model answers one yes/no question per move; the move with the highest P(yes) is played. Two framings, as in JevSpatial: q the consequence is written INSIDE each question ("Moving up: the snake moves closer to the food. Should the snake move up?"), minimal status state; s the consequences are in the STATE ({"if the snake moves up": ...}), plain questions ("Is moving up a good move?"). Games (toy physics, fixed seeds, endless: a crash restarts the game): Snake 12x12, Dino, Breakout, Whac-A-Mole. Reported per game and framing, over N decisions: apples (Snake), obstacles cleared (Dino), bricks (Breakout), moles (Whac-A-Mole), the failures, and the median decision latency in ms. Wording and consequences come from spatial_bench.py. # any Python decider: module.path:factory, factory() returns an object with .decide(state, questions) or a function: python play_bench.py --decider python --decider-target my_module:make_decider --decisions 300 # a /v1/systemone server (any server speaking the same protocol): python play_bench.py --decider http --url http://127.0.0.1:8017 --decisions 300 # references: --decider oracle (always the best move), --decider random """ import argparse import json import random import statistics import sys import time from pathlib import Path ROOT = Path(__file__).resolve().parent sys.path.insert(0, str(ROOT)) import spatial_bench as S # noqa: E402 GAMES = ("Snake", "Dino", "Breakout", "Whac-A-Mole") # ------------------------------------------------------------------ environments class Snake: name, n = "Snake", 12 def __init__(self, seed): self.rng = random.Random(seed) self.m = {"apples": 0, "deaths": 0} self.reset() def reset(self): c = (self.n // 2, self.n // 2) self.walk = [(c[0], c[1] - 2), (c[0], c[1] - 1), c] self.age = 0 self.spawn() def spawn(self): free = [(r, c) for r in range(self.n) for c in range(self.n) if (r, c) not in self.walk] self.food = self.rng.choice(free) def sit(self): moves = S.snake_moves(self.walk, self.food, self.n) status = {"game": "Snake", "board": f"{self.n}x{self.n}", "length": len(self.walk), "apples eaten": len(self.walk) - 3, "turn": self.age} return {"game": "Snake", "unit": "the snake", "status": status, "moves": moves} def act(self, mid): self.age += 1 dr, dc = S.DIRS[mid] h = self.walk[-1] new = (h[0] + dr, h[1] + dc) if not (0 <= new[0] < self.n and 0 <= new[1] < self.n) or new in self.walk[1:] or self.age > 600: self.m["deaths"] += 1 return self.reset() self.walk.append(new) if new == self.food: self.m["apples"] += 1 self.spawn() else: self.walk.pop(0) class Dino: name, v, lo = "Dino", 8, 34 def __init__(self, seed): self.rng = random.Random(seed) self.m = {"obstacles_cleared": 0, "crashes": 0} self.obs = [] # [distance of the front edge, kind] self.busy = 0 self.score = 0 self.fill() def fill(self): last = self.obs[-1][0] if self.obs else 150 while len(self.obs) < 3: last += self.rng.randint(150, 260) self.obs.append([last, self.rng.choice(list(S.DINO_OBS))]) def phase(self): if not self.obs or self.obs[0][0] >= 160: return "clear", "clear" d, kind = self.obs[0] return ("now" if d < self.lo + self.v else "far"), kind def advance(self): for o in self.obs: o[0] -= self.v self.score += 1 def settle(self): """Obstacles that reached the dinosaur without an action hit it.""" while self.obs and self.obs[0][0] < self.lo: self.m["crashes"] += 1 self.obs.pop(0) self.fill() def sit(self): ph, kind = self.phase() moves = S.dino_moves(kind, ph) status = {"game": "Dino", "score": self.score, "speed": "medium"} return {"game": "Dino", "unit": "the dinosaur", "status": status, "moves": moves} def act(self, mid): ph, kind = self.phase() need = "run" if ph != "now" else ("duck" if S.DINO_OBS[kind] == "high" else "jump") if ph == "now": self.obs.pop(0) self.m["obstacles_cleared" if mid == need else "crashes"] += 1 self.fill() elif mid != need: # acted too early: busy for a few ticks, obstacles keep coming for _ in range(6): self.advance() self.settle() return self.advance() self.settle() class Breakout: name, W, w, step = "Breakout", 200, 40, 20 def __init__(self, seed): self.rng = random.Random(seed) self.m = {"bricks": 0, "balls_lost": 0} self.p = 100 self.launch() def launch(self): self.t = self.rng.randint(5, 9) reach = (self.t - 1) * self.step self.x = min(self.W - 10, max(10, self.p + self.rng.randint(-reach, reach))) def sit(self): moves = S.breakout_moves(self.p, self.x, self.w, self.step, self.W) status = {"game": "Breakout", "lives": 3, "bricks left": 40 - self.m["bricks"] % 40} return {"game": "Breakout", "unit": "the paddle", "status": status, "moves": moves} def act(self, mid): d = {"left": -1, "stay": 0, "right": 1}[mid] self.p = min(self.W - self.w // 2, max(self.w // 2, self.p + d * self.step)) self.t -= 1 if self.t == 0: self.m["bricks" if abs(self.p - self.x) <= self.w // 2 else "balls_lost"] += 1 self.launch() class Mole: name = "Whac-A-Mole" def __init__(self, seed): self.rng = random.Random(seed) self.m = {"moles": 0, "bombs_hit": 0, "empty_swings": 0} self.kinds = {} def sit(self): while True: s = S.mole_sit(self.rng) if s: break self.kinds = {m["id"]: ("mole" if m["good"] else "bomb" if m["rank"] == 0 else "empty") for m in s["moves"]} return s def act(self, mid): k = self.kinds[mid] self.m["moles" if k == "mole" else "bombs_hit" if k == "bomb" else "empty_swings"] += 1 ENVS = {"Snake": Snake, "Dino": Dino, "Breakout": Breakout, "Whac-A-Mole": Mole} # ------------------------------------------------------------------ deciders def p_yes(a) -> float: """P(yes) from a System One noul answer ({"noul": p}, or a probabilities / probability variant).""" if isinstance(a, (int, float)): return float(a) if isinstance(a, dict): for k in ("noul", "probability", "p_yes", "yes"): if k in a: return float(a[k]) pr = a.get("probabilities") if isinstance(pr, dict): for k in ("true", "yes", "True"): if k in pr: return float(pr[k]) raise ValueError(f"no P(yes) in {a!r}") def make_python(target): """`module.path:factory`: import the module, call factory(); use the result's .decide(state, questions), or the result itself when it is a function.""" import importlib import os sys.path.insert(0, os.getcwd()) mod, _, name = target.partition(":") if not name: raise SystemExit("--decider-target must look like module.path:factory") obj = getattr(importlib.import_module(mod), name)() return obj.decide if hasattr(obj, "decide") else obj def make_http(url="http://127.0.0.1:8017", key=None, model="jev-latest", timeout=30): import urllib.request def decide(state, questions): req = urllib.request.Request(url.rstrip("/") + "/v1/systemone", method="POST", data=json.dumps({"model": model, "state": state, "questions": questions}).encode(), headers={"Content-Type": "application/json", **({"Authorization": f"Bearer {key}"} if key else {})}) out = json.load(urllib.request.urlopen(req, timeout=timeout)) for wrap in ("answers", "results", "output"): # tolerate an envelope around the {question id: answer} map if isinstance(out, dict) and wrap in out and isinstance(out[wrap], dict) and set(questions) <= set(out[wrap]): return out[wrap] return out return decide def make_random(seed=0): rng = random.Random(seed) return lambda state, questions: {k: {"type": "noul", "noul": rng.random()} for k in questions} # ------------------------------------------------------------------ the loop def play(game: str, framing: str, decide, decisions: int, seed: int = 0, oracle: bool = False) -> dict: env = ENVS[game](seed) rng = random.Random(f"{seed}:frame") lat = [] t_all = time.perf_counter() for i in range(decisions): sit = env.sit() item = (S.render_q if framing == "q" else S.render_s)(sit, rng, i) keys = [m["id"] for m in sit["moves"]] if oracle: mid = max(sit["moves"], key=lambda m: m["rank"])["id"] else: t = time.perf_counter() ans = decide(item["state"], {k: q for k, q in item["questions"].items() if k in keys}) lat.append((time.perf_counter() - t) * 1000) mid = max(keys, key=lambda k: p_yes(ans[k])) env.act(mid) out = {"game": game, "framing": framing, "decisions": decisions, **env.m, "median_decision_ms": round(statistics.median(lat), 2) if lat else None, "wall_s": round(time.perf_counter() - t_all, 2)} return out def main(): ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) ap.add_argument("--decider", choices=["python", "http", "oracle", "random"], default="python") ap.add_argument("--decider-target", default=None, help="module.path:factory for --decider python") ap.add_argument("--url", default="http://127.0.0.1:8017") ap.add_argument("--key", default=None) ap.add_argument("--decisions", type=int, default=300, help="decisions per game and framing") ap.add_argument("--games", default=",".join(GAMES)) ap.add_argument("--framings", default="q,s") ap.add_argument("--seed", type=int, default=0) ap.add_argument("--out", default=None) a = ap.parse_args() decide = {"python": lambda: make_python(a.decider_target), "http": lambda: make_http(a.url, a.key), "random": make_random, "oracle": lambda: None}[a.decider]() if decide is not None and a.decider != "random": # warm-up: compile / load outside the timed loop play("Snake", "q", decide, 3, seed=999) rows = [] for g in a.games.split(","): for f in a.framings.split(","): r = play(g, f, decide, a.decisions, a.seed, oracle=a.decider == "oracle") rows.append(r) print(json.dumps(r), flush=True) if a.out: json.dump(rows, open(a.out, "w"), indent=1) if __name__ == "__main__": main()