snowy-yang
a7725f8812
在引入 uv 依赖管理、ruff 静态检查与格式化、pyright 类型检查之前， 先把当前可正常运行的状态固定下来，作为后续大规模自动改动的回滚点。 此时状态：97 项测试全绿，HTTP + WebSocket 多牧场服务器可用。
222 lines
8.2 KiB
Python
222 lines
8.2 KiB
Python
"""规则机器人玩家——"AI 也能玩"的最小可用示例。
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它的意义不在于"内置了 AI"，而在于证明一件事：
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**服务器把状态和合法动作都摊开了，外面是谁在决策根本不重要。**
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这个机器人做决策的方式是"给候选动作打分"，而一个 LLM 做决策的方式是
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"读同一份 ``/api/actions``，用语言模型选一个"。两者的接口调用序列一模一样。
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所以：
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* :meth:`RuleBot.choose` 里那段打分逻辑 = 可以整体替换成 LLM 的"策略函数"；
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* :meth:`RuleBot.step` 的执行路径 = LLM 将来要走的那条路（读动作 → 执行 → 看回执）。
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它只依赖 :mod:`farm.client`，因此既能在本机跑，也能从别处连上服务器跑——
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换个 ``--url`` 就是一个独立的外挂玩家。
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"""
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from __future__ import annotations
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import random
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import threading
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import time
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from typing import Any, Dict, List, Optional
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from .engine import GameError
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class RuleBot:
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"""一个会种地、会养殖、会投资的规则机器人。"""
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def __init__(self, client, verbose: bool = True, pause: float = 0.25,
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name: str = "bot") -> None:
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self.client = client # 任何与 RemoteFarm 接口一致的对象
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self.verbose = verbose
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self.pause = pause
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self.name = name
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self.done: List[str] = []
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self.failures = 0
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self.actions_executed = 0
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self._stop = threading.Event()
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# ------------------------------------------------------------ 输出
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def say(self, text: str) -> None:
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if self.verbose:
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print(f"[{time.strftime('%H:%M:%S')}] {text}", flush=True)
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def stop(self) -> None:
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self._stop.set()
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@property
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def stopped(self) -> bool:
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return self._stop.is_set()
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# ------------------------------------------------------------ 打分
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def score(self, action: Dict[str, Any], state: Dict[str, Any],
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catalog: Dict[str, Any]) -> float:
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"""给一个候选动作打分。分数越高越先执行。
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这张表就是整个"策略"：先做免费的，再喂饱牲畜，再补水，再扩张。
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换成 LLM 时，把这里换成一次模型调用即可，其余代码不用动。
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"""
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name = action.get("action")
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params = action.get("params") or {}
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gold = state.get("gold", 0)
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feed = state.get("feed", 0)
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ap = state.get("ap", 0)
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if name == "harvest":
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return 100.0
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if name == "collect":
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return 96.0
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if name == "feed":
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return 92.0
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if name == "water":
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return 84.0
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if name == "plant":
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# 优先种"每天净收益"最高的作物
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crop = params.get("crop")
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entry = next((c for c in catalog.get("crops", []) if c["key"] == crop), None)
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per_day = entry["profit_per_day"] if entry else 0.0
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return 60.0 + max(0.0, per_day)
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if name == "buy_feed":
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# 饲料快见底才补，且别把钱花光
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if feed <= 2 and gold >= 200:
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return 40.0
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return 5.0
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if name == "buy_plot":
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price = state.get("next_plot_price") or 0
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# 地太贵就别买，留下周转资金
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if price > 0 and gold >= price * 3:
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return 34.0
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return 4.0
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if name == "buy_animal":
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info = next((a for a in catalog.get("animals", [])
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if a["key"] == params.get("kind")), None)
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cost = info["cost"] if info else 0
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if cost and gold >= cost * 3 and ap >= 1:
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return 30.0 + (info.get("profit_per_day", 0) if info else 0)
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return 3.0
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return 1.0
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def choose(self) -> Optional[Dict[str, Any]]:
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"""在服务器给出的合法动作里挑一个最优的。"""
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actions = self.client.available_actions()
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if not actions:
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return None
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state = self.client.snapshot()
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catalog = self.client.catalog()
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best = max(actions, key=lambda item: self.score(item, state, catalog))
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if self.score(best, state, catalog) <= 1.0:
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return None
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return best
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# ------------------------------------------------------------ 单步
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def step(self) -> Optional[str]:
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"""执行一个动作，返回动作名；没得做或做不了返回 None。"""
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action = self.choose()
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if action is None:
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return None
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name = action["action"]
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params = action.get("params") or {}
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try:
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result = self.client._action(name, params) # 走通用动作入口
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except GameError as exc:
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self.failures += 1
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if exc.code == "cooldown":
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# 休息有冷却；这是正常节奏，不该记成失败
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self.failures -= 1
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self.say(f"冷却中… {exc.message}")
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time.sleep(1.0)
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return None
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self.say(f"✗ {name} 失败（{exc.code}）：{exc.message}")
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return None
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self.actions_executed += 1
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self.done.append(name)
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self.say(f"✓ {name} {params} → {result.get('message', '')}")
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if self.pause:
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time.sleep(self.pause)
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return name
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def rest(self) -> bool:
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"""行动点用完时推进一天。"""
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try:
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result = self.client.sleep()
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except GameError as exc:
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if exc.code == "cooldown":
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time.sleep(1.0)
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return True
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self.say(f"✗ sleep 失败：{exc.message}")
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return False
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self.say(f"— {result.get('message', '进入下一天')}")
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return True
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# ------------------------------------------------------------ 主循环
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def run(self, rounds: int = 0, max_minutes: float = 0.0,
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idle_limit: int = 8) -> Dict[str, Any]:
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"""跑机器人。
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``rounds`` 为 0 表示不限轮数；``max_minutes`` 为 0 表示不限时间。
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"""
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deadline = (time.time() + max_minutes * 60) if max_minutes > 0 else None
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start_day = self.client.snapshot().get("day", 1)
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idle = 0
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round_no = 0
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self.say(f"机器人 {self.name} 上线（第 {start_day} 天，"
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f"{self.client.transport_label()}）")
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while not self._stop.is_set():
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round_no += 1
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if rounds and round_no > rounds:
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break
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if deadline and time.time() > deadline:
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self.say("到时间了，收工。")
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break
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state = self.client.snapshot()
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if state.get("ap", 0) <= 0:
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# 行动点用完：休息推进一天，顺便等冷却
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self.rest()
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continue
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did = self.step()
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if did is None:
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idle += 1
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if idle >= idle_limit:
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self.say("暂时没有可做的事，休息一天再看。")
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self.rest()
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idle = 0
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else:
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time.sleep(0.4)
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else:
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idle = 0
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end_day = self.client.snapshot().get("day", start_day)
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summary = {
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"name": self.name,
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"days": end_day - start_day,
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"actions": self.actions_executed,
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"failures": self.failures,
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"net_worth": self.client.snapshot().get("net_worth"),
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"gold": self.client.snapshot().get("gold"),
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}
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self.say(f"机器人 {self.name} 下线：跑了 {summary['days']} 天、"
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f"执行 {summary['actions']} 个动作、净资产 {summary['net_worth']}")
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return summary
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def random_bot(client, **kwargs) -> RuleBot:
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"""随机策略机器人：用来做对照实验（证明"有策略"确实比乱按强）。"""
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bot = RuleBot(client, **kwargs)
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original = bot.choose
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def choose() -> Optional[Dict[str, Any]]:
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actions = client.available_actions()
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if not actions:
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return None
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return random.choice(actions)
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bot.choose = choose # type: ignore[assignment]
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bot.name = kwargs.get("name", "random")
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del original
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return bot
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