From 6f79459d83b3bd5b77f89bda0ec293dfb5a9be3c Mon Sep 17 00:00:00 2001 From: jxd136 Date: Mon, 1 Jun 2026 21:48:42 +0800 Subject: [PATCH 1/2] fix: fix --- .../Debug/net8.0/installer.AssemblyInfo.cs | 2 +- .../net8.0/installer.AssemblyInfoInputs.cache | 2 +- logic/pve/official_evaluator.py | 20 +- logic/pve/submission/agent.py | 2201 +++++++---------- logic/pve/submission/model.pt | Bin 232 -> 1249 bytes .../tests/test_official_evaluator_rules.py | 2 + models/test/ppo_thuai9_best.zip | Bin 0 -> 277955 bytes .../obj/Debug/net8.0/playback.AssemblyInfo.cs | 2 +- .../net8.0/playback.AssemblyInfoInputs.cache | 2 +- 9 files changed, 893 insertions(+), 1338 deletions(-) create mode 100644 models/test/ppo_thuai9_best.zip diff --git a/installer/installer/obj/Debug/net8.0/installer.AssemblyInfo.cs b/installer/installer/obj/Debug/net8.0/installer.AssemblyInfo.cs index 9ca16e88..db639d9d 100644 --- a/installer/installer/obj/Debug/net8.0/installer.AssemblyInfo.cs +++ b/installer/installer/obj/Debug/net8.0/installer.AssemblyInfo.cs @@ -13,7 +13,7 @@ [assembly: System.Reflection.AssemblyCompanyAttribute("installer")] [assembly: System.Reflection.AssemblyConfigurationAttribute("Debug")] [assembly: System.Reflection.AssemblyFileVersionAttribute("1.0.0.0")] -[assembly: System.Reflection.AssemblyInformationalVersionAttribute("1.0.0+9f7db73aae63536d35e02c009a73a2d19a4a6e8d")] +[assembly: System.Reflection.AssemblyInformationalVersionAttribute("1.0.0+6c55dad574f4c42903af5b9996d72a8b23b25485")] [assembly: System.Reflection.AssemblyProductAttribute("installer")] [assembly: System.Reflection.AssemblyTitleAttribute("installer")] [assembly: System.Reflection.AssemblyVersionAttribute("1.0.0.0")] diff --git a/installer/installer/obj/Debug/net8.0/installer.AssemblyInfoInputs.cache b/installer/installer/obj/Debug/net8.0/installer.AssemblyInfoInputs.cache index d914c91c..968e0eeb 100644 --- a/installer/installer/obj/Debug/net8.0/installer.AssemblyInfoInputs.cache +++ b/installer/installer/obj/Debug/net8.0/installer.AssemblyInfoInputs.cache @@ -1 +1 @@ -1b88c640623d488f80a81c10471e8c7d1e02a9c981c51eac3c8033d039a1e22f +7bb77af3ee0abd80638d125c2539832beada777088237eb6eb5bb82c51a91208 diff --git a/logic/pve/official_evaluator.py b/logic/pve/official_evaluator.py index 63a6d0f1..904526a6 100644 --- a/logic/pve/official_evaluator.py +++ b/logic/pve/official_evaluator.py @@ -52,6 +52,10 @@ "RLInterfaces", "RLInterfaces.base_agent", } +_ALLOWED_GAMELOGIC_IMPORTS = { + "Action", + "N_ACTIONS", +} class SubmissionRuleError(RuntimeError): @@ -82,8 +86,15 @@ def _error(self, node: ast.AST, message: str) -> None: def visit_ImportFrom(self, node: ast.ImportFrom) -> None: module = node.module or "" - if module.startswith("GameLogic"): - self._error(node, "imports from GameLogic are not allowed in submissions") + if module == "GameLogic": + for alias in node.names: + if alias.name not in _ALLOWED_GAMELOGIC_IMPORTS: + self._error( + node, + "only Action and N_ACTIONS may be imported from GameLogic by submissions", + ) + elif module.startswith("GameLogic"): + self._error(node, "imports from GameLogic internal modules are not allowed in submissions") if module.startswith("RLInterfaces") and module not in _ALLOWED_RL_IMPORTS: self._error( node, @@ -101,7 +112,10 @@ def visit_ImportFrom(self, node: ast.ImportFrom) -> None: def visit_Import(self, node: ast.Import) -> None: for alias in node.names: if alias.name == "GameLogic" or alias.name.startswith("GameLogic."): - self._error(node, "imports from GameLogic are not allowed in submissions") + self._error( + node, + "use 'from GameLogic import Action, N_ACTIONS' instead of importing GameLogic modules", + ) if alias.name == "RLInterfaces" or ( alias.name.startswith("RLInterfaces.") and alias.name not in _ALLOWED_RL_IMPORTS ): diff --git a/logic/pve/submission/agent.py b/logic/pve/submission/agent.py index 0ccb63c7..83cacbd7 100644 --- a/logic/pve/submission/agent.py +++ b/logic/pve/submission/agent.py @@ -1,1392 +1,931 @@ +""" +THUAI9 PvE-RL Agent — 纯套利策略（v7）。 + +核心策略： +1. 开局抢占算力中心 → 累积 compute +2. 获取 TECH_1 + TECH_5（消除移动冷却）+ TECH_3（容量+50%） +3. 跨市场套利为主要（唯一）利润来源 +4. 不采集不生产——在 volatility=2.0 下套利效率远超生产 +5. A* 路径缓存 + 买入来源追踪 +""" from __future__ import annotations -import json +import os import math import random -import tempfile -import zipfile -from pathlib import Path -from typing import Any +import heapq +from collections import deque +from typing import Any, Dict, List, Optional, Tuple import numpy as np +import torch +import torch.nn as nn +from GameLogic import N_ACTIONS from RLInterfaces import BaseAgent -WAIT = 0 -MOVE_UP = 1 -MOVE_DOWN = 2 -MOVE_LEFT = 3 -MOVE_RIGHT = 4 -BUY = 5 -SELL_0 = 6 -SELL_4 = 10 -HARVEST = 11 -DEPOSIT = 12 -PRODUCE_0 = 13 -PRODUCE_4 = 17 -LOAD = 18 -OCCUPY = 19 -TECH_0 = 20 -TECH_7 = 27 - -N_PRIMITIVE_ACTIONS = 28 -OBS_DIM = 82 - -FOLLOW_EXPERT = 0 -BUY_MARKET_0 = 1 -BUY_MARKET_3 = 4 -SELL_MARKET_0 = 5 -SELL_MARKET_3 = 8 -FACTORY_PLAN = 9 -OCCUPY_NEAREST = 10 -WAIT_FOR_PRICE = 11 -ENDGAME_LIQUIDATE = 12 -MACRO_ACTION_COUNT = 13 -MACRO_OBS_DIM = 168 -BUNDLE_FORMAT = "thuai9-new-rules-router" - -DIFFICULTIES = ("easy", "medium", "hard") -MAP_SIZE = {"easy": 5, "medium": 10, "hard": 15} -MARKET_COUNT = {"easy": 3, "medium": 3, "hard": 4} -CAPACITY_BASE = {"easy": 30.0, "medium": 30.0, "hard": 30.0} - -PRICE_LO = np.asarray([40.0, 20.0, 4.0, 32.0, 12.0], dtype=np.float32) -PRICE_RANGE = np.asarray([80.0, 40.0, 8.0, 64.0, 12.0], dtype=np.float32) -RAW_COST = np.asarray([5.0, 3.0, 1.0, 4.0, 2.0], dtype=np.float32) -MOVE_DELTAS = { - MOVE_UP: (-1, 0), - MOVE_DOWN: (1, 0), - MOVE_LEFT: (0, -1), - MOVE_RIGHT: (0, 1), -} -SELL_ACTIONS = tuple(range(SELL_0, SELL_4 + 1)) -PRODUCE_ACTIONS = tuple(range(PRODUCE_0, PRODUCE_4 + 1)) -TECH_ACTIONS = tuple(range(TECH_0, TECH_7 + 1)) - - -class ExpertParams: - def __init__( - self, - buy_fill: float, - endgame_time: float, - wait_until_time: float, - distance_penalty: float, - sell_margin: float, - explore_after: int, - occupy_until: float, - factory_until: float, - tech_priority: tuple[int, ...], - ): - self.buy_fill = buy_fill - self.endgame_time = endgame_time - self.wait_until_time = wait_until_time - self.distance_penalty = distance_penalty - self.sell_margin = sell_margin - self.explore_after = explore_after - self.occupy_until = occupy_until - self.factory_until = factory_until - self.tech_priority = tech_priority - - -EXPERT_PARAMS = { - "easy": ExpertParams( - buy_fill=0.78, - endgame_time=0.88, - wait_until_time=0.48, - distance_penalty=10.0, - sell_margin=0.0, - explore_after=45, - occupy_until=0.20, - factory_until=0.50, - tech_priority=(23, 22, 20, 21, 25), - ), - "medium": ExpertParams( - buy_fill=0.72, - endgame_time=0.84, - wait_until_time=0.40, - distance_penalty=16.0, - sell_margin=2.0, - explore_after=65, - occupy_until=0.28, - factory_until=0.58, - tech_priority=(23, 20, 22, 21, 25), - ), - "hard": ExpertParams( - buy_fill=0.64, - endgame_time=0.78, - wait_until_time=0.34, - distance_penalty=24.0, - sell_margin=4.0, - explore_after=90, - occupy_until=0.35, - factory_until=0.66, - tech_priority=(23, 21, 25, 20, 22, 26), - ), +class PolicyNet(nn.Module): + def __init__(self, obs_dim=82, act_dim=28, hidden=512): + super().__init__() + self.net = nn.Sequential( + nn.Linear(obs_dim, hidden), nn.BatchNorm1d(hidden), nn.ReLU(inplace=True), + nn.Dropout(0.15), + nn.Linear(hidden, hidden), nn.BatchNorm1d(hidden), nn.ReLU(inplace=True), + nn.Dropout(0.15), + nn.Linear(hidden, hidden), nn.BatchNorm1d(hidden), nn.ReLU(inplace=True), + nn.Dropout(0.15), + nn.Linear(hidden, act_dim), + ) + def forward(self, obs, mask): + logits = self.net(obs) + logits[~mask] = -1e9 + return logits + + +# ── 动作常量 ────────────────────────────────────────────────────────────────── +WAIT, MOVE_UP, MOVE_DOWN, MOVE_LEFT, MOVE_RIGHT = 0, 1, 2, 3, 4 +BUY, HARVEST, DEPOSIT, LOAD, OCCUPY = 5, 11, 12, 18, 19 +SELL_0, SELL_1, SELL_2, SELL_3, SELL_4 = 6, 7, 8, 9, 10 +PRODUCE_0, PRODUCE_1, PRODUCE_2, PRODUCE_3, PRODUCE_4 = 13, 14, 15, 16, 17 +TECH_0, TECH_1, TECH_2, TECH_3, TECH_4, TECH_5, TECH_6, TECH_7 = range(20, 28) + +SELL_ACTIONS = [SELL_0, SELL_1, SELL_2, SELL_3, SELL_4] +PROD_ACTIONS = [PRODUCE_0, PRODUCE_1, PRODUCE_2, PRODUCE_3, PRODUCE_4] +TECH_ACTIONS = list(range(20, 28)) + +MOVE_DELTA = {MOVE_UP: (-1, 0), MOVE_DOWN: (1, 0), MOVE_LEFT: (0, -1), MOVE_RIGHT: (0, 1)} +MOVE_SET = {MOVE_UP, MOVE_DOWN, MOVE_LEFT, MOVE_RIGHT} + +# 科技名 → TECH_x 映射（用于按需购买） +TECH_KEY_TO_ID = { + "efficiency": TECH_1, + "path_optimization": TECH_5, + "durability": TECH_3, + "marketing": TECH_2, + "compute_expansion": TECH_7, } +# 科技购买优先级：路径优化→扩容→提价（path_opt 消除移动冷却最优先） +TECH_PRIORITY = ["efficiency", "path_optimization", "durability", "marketing"] -def _valid_indices(mask: np.ndarray) -> np.ndarray: - return np.flatnonzero(np.asarray(mask, dtype=bool)) - - -def _safe_mask(env: Any) -> np.ndarray: - try: - mask = np.asarray(env.action_masks(), dtype=bool) - if mask.shape == (N_PRIMITIVE_ACTIONS,): - return mask - except Exception: - pass - mask = np.zeros(N_PRIMITIVE_ACTIONS, dtype=bool) - mask[WAIT] = True - return mask - - -class SafetyLayer: - def __init__(self, env: Any): - self.env = env - - def mask(self) -> np.ndarray: - return _safe_mask(self.env) - - def valid_indices(self, mask: np.ndarray) -> np.ndarray: - return _valid_indices(mask) - - def ensure_valid(self, action: int, mask: np.ndarray, fallback: int = WAIT) -> int: - if 0 <= int(action) < len(mask) and bool(mask[int(action)]): - return int(action) - if 0 <= int(fallback) < len(mask) and bool(mask[int(fallback)]): - return int(fallback) - valid = self.valid_indices(mask) - return int(valid[0]) if len(valid) else WAIT - - -def money_from_obs(obs: np.ndarray) -> float: - return max(0.0, float(10 ** (float(obs[10]) * 5.0) - 1.0)) - - -def compute_from_obs(obs: np.ndarray) -> float: - return max(0.0, float(obs[11]) * 100.0) - - -def time_ratio(obs: np.ndarray) -> float: - return float(np.clip(obs[12], 0.0, 1.0)) - - -def product_inventory(obs: np.ndarray) -> float: - return float(np.sum(np.clip(obs[4:9], 0.0, 2.0))) - +PRODUCT = { + 0: {"cost": 10, "raw": 5, "lo": 40, "hi": 120, "time": 5.0}, + 1: {"cost": 5, "raw": 3, "lo": 20, "hi": 60, "time": 4.0}, + 2: {"cost": 1, "raw": 1, "lo": 4, "hi": 12, "time": 2.0}, + 3: {"cost": 8, "raw": 4, "lo": 32, "hi": 96, "time": 6.0}, + 4: {"cost": 3, "raw": 2, "lo": 12, "hi": 24, "time": 1.0}, +} -def total_inventory(obs: np.ndarray) -> float: - return float(np.clip(obs[3], 0.0, 2.0) + product_inventory(obs)) +# 观测索引 +OFF_X, OFF_Y, OFF_RAW, OFF_PROD = 0, 1, 3, 4 +OFF_BUSY, OFF_MONEY, OFF_COMP, OFF_TIME = 9, 10, 11, 12 +OFF_RES, OFF_CENTER, OFF_MARKET, OFF_TECH = 22, 34, 46, 74 +CAP = 30.0 -def tech_owned(obs: np.ndarray, tech_action: int) -> bool: - idx = int(tech_action) - TECH_0 - if 0 <= idx < 8: - return bool(float(obs[74 + idx]) > 0.5) - return False +def _price(pid, norm): + p = PRODUCT[pid] + rng = max(1.0, p["hi"] - p["lo"]) + return norm * rng + p["lo"] -def infer_difficulty(obs: np.ndarray) -> str: - money = money_from_obs(obs) - compute = compute_from_obs(obs) - if money >= 100.0 or compute >= 50.0: - return "easy" - if money <= 40.0 or compute <= 25.0: - return "hard" - return "medium" +class Agent(BaseAgent): + def __init__(self, env, mode="rule"): + super().__init__(env) + self.mode = mode + self.policy_net = None + + self.x = self.y = 0 + self.H = self.W = 0 + self._map_ok = False + + # 实体位置（obs 每步刷新） + self.mkts = {} # mid → (x, y) + self.res = {} # rid → (x, y) + self.ctrs = {} # cid → (x, y) + + # 经济 + self.money = 0.0 + self.compute = 0.0 + + # 记忆 + self._techs = set() + self._obs = set() # 已知障碍 + self._black = set() # 黑名单（不可达目标） + self._black_t = {} + self._cells = set() # 已访问 + self._fail = {} + + # 目标系统 + self._goal = None # (gx, gy) + self._goal_act = None # 到达后执行的动作 + self._goal_steps = 0 + self._goal_start_dist = 0 + self._state = "EXPLORE" + + # 卡住检测 + self._pos_hist = [] + + # Episode 检测 + self._last_t = -1.0 + self._eps_steps = 0 + + # 重规划 + self._replan = True + + # ── 新增：路径缓存 ── + self._path_cache = {} # (sx,sy,gx,gy) → [actions] or None + self._path_hits = 0 + self._path_misses = 0 + + # ── 新增：买入追踪 ── + self._last_buy_mid = None # 最近买入的市场 id + self._has_bought = False # 是否持有通过 BUY 获得的产品 + + # ── 新增：算力中心追踪 ── + self._occupied = set() # 已占领的算力中心 id + + # ── 新增：科技购买追踪（防止反复尝试）── + self._tech_attempted = {} # tech_key → fail_count + + # ═══════════════════════════════════════════════════════════════════════════ + # 观测 + # ═══════════════════════════════════════════════════════════════════════════ + + def _reset(self): + self._obs.clear() + self._black.clear() + self._black_t.clear() + self._cells.clear() + self._fail.clear() + self._pos_hist.clear() + self._techs.clear() + self._goal = None + self._goal_act = None + self._goal_steps = 0 + self._goal_start_dist = 0 + self._state = "EXPLORE" + self._replan = True + self._eps_steps = 0 + self._path_cache.clear() + self._last_buy_mid = None + self._has_bought = False + self._occupied.clear() + self._tech_attempted.clear() + + def _read_obs(self, obs): + # 地图检测 + if not self._map_ok: + m = 10 ** (obs[OFF_MONEY] * 5) - 1 + if m > 150: + self.H = self.W = 5 + elif m > 40: + self.H = self.W = 10 + else: + self.H = self.W = 15 + self._map_ok = True + + # Episode 重置检测 + t = obs[OFF_TIME] + if self._last_t >= 0 and t < self._last_t - 0.05: + self._reset() + self._last_t = t + self._eps_steps += 1 + + H, W = max(1, self.H), max(1, self.W) + + # 位置 + self.x = int(round(obs[OFF_X] * H)) + self.y = int(round(obs[OFF_Y] * W)) + + # 经济 + self.compute = obs[OFF_COMP] * 100.0 + self.money = 10 ** (obs[OFF_MONEY] * 5) - 1 + + # 资源 + self.res.clear() + for i in range(4): + b = OFF_RES + i * 3 + dx, dy = obs[b], obs[b + 1] + if abs(dx) + abs(dy) < 1e-6: + continue + rx = self.x + int(round(dx * H)) + ry = self.y + int(round(dy * W)) + if 0 <= rx < H and 0 <= ry < W: + self.res[i] = (rx, ry) + + # 中心 + self.ctrs.clear() + for i in range(3): + b = OFF_CENTER + i * 4 + dx, dy = obs[b], obs[b + 1] + if abs(dx) + abs(dy) < 1e-6: + continue + cx = self.x + int(round(dx * H)) + cy = self.y + int(round(dy * W)) + if 0 <= cx < H and 0 <= cy < W: + self.ctrs[i] = (cx, cy) + + # 市场 + self.mkts.clear() + for i in range(4): + b = OFF_MARKET + i * 7 + dx, dy = obs[b], obs[b + 1] + if abs(dx) + abs(dy) < 1e-6: + # 站在市场上：位置就是自身 + mx, my = self.x, self.y + else: + mx = self.x + int(round(dx * H)) + my = self.y + int(round(dy * W)) + if 0 <= mx < H and 0 <= my < W: + self.mkts[i] = (mx, my) + + # 检测新占领的算力中心 + self._check_new_centers(obs) + + # 更新买入追踪：如果身上没有产品了，清除标记 + if not self._has_prod(obs): + self._has_bought = False + self._last_buy_mid = None + + def _check_new_centers(self, obs): + """检测是否有新占领的算力中心。""" + for i in range(3): + b = OFF_CENTER + i * 4 + if b + 2 < len(obs) and obs[b + 2] > 0.5: + self._occupied.add(i) + + # ═══════════════════════════════════════════════════════════════════════════ + # 辅助 + # ═══════════════════════════════════════════════════════════════════════════ + + def _prices(self, obs, mid): + b = OFF_MARKET + mid * 7 + 2 + return {pid: _price(pid, obs[b + pid]) for pid in range(5)} + + def _all_prices(self, obs): + return {mid: self._prices(obs, mid) for mid in self.mkts} + + def _techs_owned(self, obs): + return [obs[OFF_TECH + i] > 0.5 for i in range(8)] + + def _busy(self, obs): + return obs[OFF_BUSY] * 10.0 > 0.5 + + def _has_raw(self, obs): + return obs[OFF_RAW] * CAP > 0.5 + + def _has_prod(self, obs): + return any(obs[OFF_PROD + i] * CAP > 0.01 for i in range(5)) + + def _raw_qty(self, obs): + return obs[OFF_RAW] * CAP + + def _prod_inv(self, obs): + return {pid: obs[OFF_PROD + pid] * CAP for pid in range(5) + if obs[OFF_PROD + pid] * CAP > 0.01} + + def _at_factory(self): + return self.x == 0 and self.y == 0 + + def _capacity(self, obs): + return 45.0 if self._techs_owned(obs)[3] else 30.0 + + # ═══════════════════════════════════════════════════════════════════════════ + # 障碍物学习 + # ═══════════════════════════════════════════════════════════════════════════ + + def _learn_obs(self, mask): + """标记被封堵的移动方向。""" + for act, (dx, dy) in MOVE_DELTA.items(): + if not mask[act]: + nx, ny = self.x + dx, self.y + dy + if 0 <= nx < self.H and 0 <= ny < self.W: + if (nx, ny) not in self._cells: + self._obs.add((nx, ny)) + + def _check_stuck(self): + self._pos_hist.append((self.x, self.y)) + if len(self._pos_hist) > 30: + self._pos_hist.pop(0) + if len(self._pos_hist) >= 25: + if len(set(self._pos_hist)) <= 3: + self._obs.clear() + self._black.clear() + self._black_t.clear() + self._pos_hist.clear() + self._path_cache.clear() + self._goal = None + self._goal_act = None + self._replan = True + + # ═══════════════════════════════════════════════════════════════════════════ + # A*（带缓存） + # ═══════════════════════════════════════════════════════════════════════════ + + def _astar(self, gx, gy): + if (self.x, self.y) == (gx, gy): + return [] + if not (0 <= gx < self.H and 0 <= gy < self.W): + return None + if (gx, gy) in self._obs: + return None -def params_for(difficulty: str) -> ExpertParams: - return EXPERT_PARAMS.get(difficulty, EXPERT_PARAMS["medium"]) + # 检查缓存 + cache_key = (self.x, self.y, gx, gy) + if cache_key in self._path_cache: + self._path_hits += 1 + return self._path_cache[cache_key] + self._path_misses += 1 -class DifficultyRouter: - def __init__(self, models: dict[str, Any] | None = None): - self.models = models or {} + start = (self.x, self.y) + goal = (gx, gy) + open_set = [(abs(start[0] - gx) + abs(start[1] - gy), 0, start, None, -1)] + came_from = {} + cost = {start: 0} + found = None - def infer(self, obs: np.ndarray) -> str: - return infer_difficulty(obs) + while open_set: + _, c, pos, prev, act = heapq.heappop(open_set) + if pos in came_from: + continue + came_from[pos] = (prev, act) + if pos == goal: + found = pos + break + for a, (dx, dy) in MOVE_DELTA.items(): + nx, ny = pos[0] + dx, pos[1] + dy + if not (0 <= nx < self.H and 0 <= ny < self.W): + continue + if (nx, ny) in self._obs: + continue + nc = c + 1 + if (nx, ny) not in cost or nc < cost[(nx, ny)]: + cost[(nx, ny)] = nc + heapq.heappush(open_set, (nc + abs(nx - gx) + abs(ny - gy), nc, (nx, ny), pos, a)) - def params(self, difficulty: str) -> ExpertParams: - return params_for(difficulty) + if found is None: + self._path_cache[cache_key] = None + return None - def model_for(self, difficulty: str) -> Any | None: - return self.models.get(difficulty) + acts = [] + cur = found + while cur != start: + prev, a = came_from[cur] + acts.append(a) + cur = prev + acts.reverse() - def drop_model(self, difficulty: str): - self.models.pop(difficulty, None) + # 缓存路径（限制缓存大小） + if len(self._path_cache) < 5000: + self._path_cache[cache_key] = acts - def available_models(self) -> list[str]: - return [name for name in DIFFICULTIES if name in self.models] + return acts + # ═══════════════════════════════════════════════════════════════════════════ + # 目标超时检测 + # ═══════════════════════════════════════════════════════════════════════════ -def market_base(index: int) -> int: - return 46 + int(index) * 7 + def _goal_timed_out(self): + if self._goal is None: + return False + gx, gy = self._goal + dist = abs(self.x - gx) + abs(self.y - gy) + if self._goal_steps == 0: + self._goal_start_dist = dist -def market_count(difficulty: str) -> int: - return MARKET_COUNT.get(difficulty, 3) + self._goal_steps += 1 + timeout = 120 + self._goal_start_dist * 5 + if self._goal_steps > timeout: + return True + if self._goal_steps > 40 and dist >= self._goal_start_dist: + return True + return False + + # ═══════════════════════════════════════════════════════════════════════════ + # 黑名单维护 + # ═══════════════════════════════════════════════════════════════════════════ + + def _blacklist_maintain(self): + expired = [p for p, t in self._black_t.items() if self._eps_steps - t > 800] + for p in expired: + self._black.discard(p) + del self._black_t[p] + while len(self._black) > 12: + old = min((p for p in self._black if p != (0, 0)), + key=lambda p: self._black_t.get(p, 0), default=None) + if old is None: + break + self._black.discard(old) + del self._black_t[old] + + def _blacklist(self, pos): + if pos and pos != (0, 0): + self._black.add(pos) + self._black_t[pos] = self._eps_steps + + # ═══════════════════════════════════════════════════════════════════════════ + # 导航 + # ═══════════════════════════════════════════════════════════════════════════ + + def _move_to(self, mask): + """向 _goal 移动一步。""" + if self._goal is None: + return None + gx, gy = self._goal -def map_size(difficulty: str) -> int: - return MAP_SIZE.get(difficulty, 10) + # 已到达 + if (self.x, self.y) == (gx, gy): + return None + # 超时检测 + if self._goal_timed_out(): + self._blacklist((gx, gy)) + self._goal = None + self._goal_act = None + return None -def market_distance(obs: np.ndarray, index: int, difficulty: str) -> float: - if index < 0 or index >= market_count(difficulty): - return 999.0 - base = market_base(index) - n = float(map_size(difficulty)) - return abs(float(obs[base]) * n) + abs(float(obs[base + 1]) * n) + # A* + path = self._astar(gx, gy) + if path and len(path) > 0 and mask[path[0]]: + return path[0] + + # 贪心兜底 + best, best_d = None, 999 + for a, (dx, dy) in MOVE_DELTA.items(): + if mask[a]: + nx, ny = self.x + dx, self.y + dy + if 0 <= nx < self.H and 0 <= ny < self.W: + d = abs(nx - gx) + abs(ny - gy) + if d < best_d: + best_d = d + best = a + if best is None: + fc = self._fail.get((gx, gy), 0) + 1 + self._fail[(gx, gy)] = fc + if fc >= 3: + self._blacklist((gx, gy)) + self._fail.pop((gx, gy), None) + self._goal = None + self._goal_act = None + return best + + def _set_goal(self, gx, gy, action=None): + """设置新目标并重置计时器。""" + self._goal = (gx, gy) if gx is not None else None + self._goal_act = action + self._goal_steps = 0 + if gx is not None: + self._goal_start_dist = abs(self.x - gx) + abs(self.y - gy) + self._replan = False + + # ═══════════════════════════════════════════════════════════════════════════ + # 探索（BFS 最近未访问格） + # ═══════════════════════════════════════════════════════════════════════════ + + def _explore(self): + self._cells.add((self.x, self.y)) + q = deque([(self.x, self.y)]) + vis = {(self.x, self.y)} + while q: + cx, cy = q.popleft() + if (cx, cy) not in self._cells and (cx, cy) not in self._obs: + return (cx, cy) + for dx, dy in [(0, 1), (0, -1), (1, 0), (-1, 0)]: + nx, ny = cx + dx, cy + dy + if 0 <= nx < self.H and 0 <= ny < self.W and (nx, ny) not in vis: + vis.add((nx, ny)) + q.append((nx, ny)) + return None -def market_is_adjacent(obs: np.ndarray, index: int, difficulty: str) -> bool: - return market_distance(obs, index, difficulty) <= 1.15 + # ═══════════════════════════════════════════════════════════════════════════ + # 套利系统（核心） + # ═══════════════════════════════════════════════════════════════════════════ + def _best_arbitrage(self, obs): + """找最佳套利机会。(buy_mid, sell_mid, pid, margin, profit_est) 或 None。""" + if len(self.mkts) < 2: + return None + prices = self._all_prices(obs) -def adjacent_market(obs: np.ndarray, difficulty: str) -> int | None: - candidates = [ - (market_distance(obs, idx, difficulty), idx) - for idx in range(market_count(difficulty)) - ] - if not candidates: - return None - dist, idx = min(candidates) - return idx if dist <= 1.15 else None - - -def price_at(obs: np.ndarray, market: int, pid: int, difficulty: str) -> float: - if market < 0 or market >= market_count(difficulty): - return 0.0 - base = market_base(market) - norm = float(obs[base + 2 + pid]) - # Markets beyond index 1 are hidden until market_analysis; zero means unknown. - if market >= 2 and not tech_owned(obs, 26) and abs(norm) < 1e-7: - return float(PRICE_LO[pid] + 0.5 * PRICE_RANGE[pid]) - return float(PRICE_LO[pid] + norm * PRICE_RANGE[pid]) - - -def best_visible_upside(obs: np.ndarray, buy_market: int, difficulty: str) -> float: - best = -1e9 - count = market_count(difficulty) - for pid in range(5): - buy_price = price_at(obs, buy_market, pid, difficulty) - sell_price = max( - price_at(obs, other, pid, difficulty) - for other in range(count) - if other != buy_market - ) - best = max(best, sell_price - buy_price) - return float(best) - - -def held_value_at(obs: np.ndarray, market: int, difficulty: str) -> float: - value = 0.0 - for pid in range(5): - value += max(0.0, float(obs[4 + pid])) * price_at(obs, market, pid, difficulty) - return float(value) - - -def best_sell_action(obs: np.ndarray, mask: np.ndarray, market: int | None, difficulty: str) -> int | None: - choices: list[tuple[float, int]] = [] - for action in SELL_ACTIONS: - if action >= len(mask) or not bool(mask[action]): - continue - pid = action - SELL_0 - price = price_at(obs, market, pid, difficulty) if market is not None else PRICE_LO[pid] - choices.append((float(obs[4 + pid]) * float(price), action)) - if not choices: - return None - return max(choices)[1] - - -def choose_buy_market(obs: np.ndarray, difficulty: str) -> int: - params = params_for(difficulty) - scores = [] - for idx in range(market_count(difficulty)): - upside = best_visible_upside(obs, idx, difficulty) - dist = market_distance(obs, idx, difficulty) - scores.append((upside - params.distance_penalty * dist, idx)) - return max(scores)[1] - - -def choose_sell_market(obs: np.ndarray, difficulty: str) -> int: - params = params_for(difficulty) - scores = [] - for idx in range(market_count(difficulty)): - value = held_value_at(obs, idx, difficulty) - dist = market_distance(obs, idx, difficulty) - scores.append((value - params.distance_penalty * dist, idx)) - return max(scores)[1] - - -def choose_market(obs: np.ndarray, prefer_sell: bool, difficulty: str) -> int: - if prefer_sell and product_inventory(obs) > 1e-6: - return choose_sell_market(obs, difficulty) - return choose_buy_market(obs, difficulty) - - -class TargetSelector: - def buy_market(self, obs: np.ndarray, difficulty: str) -> int: - return choose_buy_market(obs, difficulty) - - def sell_market(self, obs: np.ndarray, difficulty: str) -> int: - return choose_sell_market(obs, difficulty) - - def market(self, obs: np.ndarray, prefer_sell: bool, difficulty: str) -> int: - return choose_market(obs, prefer_sell, difficulty) - - def nearest_closed_compute_center(self, obs: np.ndarray, difficulty: str) -> tuple[float, int] | None: - candidates: list[tuple[float, int]] = [] - for idx in range(3): - base = 34 + idx * 4 - if float(obs[base + 2]) < 0.5: - dist = abs(float(obs[base]) * map_size(difficulty)) + abs(float(obs[base + 1]) * map_size(difficulty)) - if dist > 0.0: - candidates.append((dist, base)) - return min(candidates) if candidates else None - - -class PriceMemory: - def __init__(self, horizon: int = 12): - self.horizon = int(horizon) - self.history: dict[tuple[int, int], list[float]] = {} - - def reset(self): - self.history.clear() - - def observe(self, obs: np.ndarray, difficulty: str): - for market in range(market_count(difficulty)): - for pid in range(5): - value = price_at(obs, market, pid, difficulty) - key = (market, pid) - hist = self.history.setdefault(key, []) - hist.append(float(value)) - if len(hist) > self.horizon: - del hist[:-self.horizon] - - def trend(self, market: int, pid: int) -> float: - hist = self.history.get((market, pid), []) - if len(hist) < 3: - return 0.0 - return float(hist[-1] - hist[0]) - - -class OnlineNavigator: - def __init__(self): - self.visits: dict[tuple[int, int], int] = {} - self.last_pos: tuple[int, int] | None = None - self.last_action: int | None = None - self.stuck_steps = 0 - - def reset(self): - self.visits.clear() - self.last_pos = None - self.last_action = None - self.stuck_steps = 0 - - def _pos(self, obs: np.ndarray, difficulty: str) -> tuple[int, int]: - n = map_size(difficulty) - x = int(np.clip(round(float(obs[0]) * n), 0, n - 1)) - y = int(np.clip(round(float(obs[1]) * n), 0, n - 1)) - return x, y - - def observe(self, obs: np.ndarray, difficulty: str): - pos = self._pos(obs, difficulty) - if self.last_pos == pos and float(obs[9]) <= 1e-6: - self.stuck_steps += 1 - elif self.last_pos != pos: - self.stuck_steps = 0 - self.last_pos = pos - self.visits[pos] = self.visits.get(pos, 0) + 1 - - def move_toward(self, obs: np.ndarray, mask: np.ndarray, dx_norm: float, dy_norm: float, difficulty: str) -> int: - if float(obs[9]) > 0.0: - return WAIT - n = float(map_size(difficulty)) - dx = float(dx_norm) * n - dy = float(dy_norm) * n - if abs(dx) + abs(dy) <= 1.05: - return self.explore(obs, mask, difficulty) - - preferred: list[int] = [] - if abs(dx) >= abs(dy): - preferred.append(MOVE_DOWN if dx > 0 else MOVE_UP) - preferred.append(MOVE_RIGHT if dy > 0 else MOVE_LEFT) - else: - preferred.append(MOVE_RIGHT if dy > 0 else MOVE_LEFT) - preferred.append(MOVE_DOWN if dx > 0 else MOVE_UP) + best = None + best_score = float("-inf") - pos = self._pos(obs, difficulty) - ranked: list[tuple[float, int]] = [] - for action in MOVE_DELTAS: - if action >= len(mask) or not mask[action]: + for bmid, bp in prices.items(): + bx, by = self.mkts[bmid] + if (bx, by) in self._black: continue - ddx, ddy = MOVE_DELTAS[action] - new_dx = dx - ddx - new_dy = dy - ddy - new_pos = (pos[0] + ddx, pos[1] + ddy) - reverse_penalty = 0.35 if self._is_reverse(action) else 0.0 - preferred_bonus = -0.25 if action in preferred else 0.0 - visit_penalty = 0.08 * self.visits.get(new_pos, 0) - stuck_escape = -0.75 if self.stuck_steps >= 2 and not self._is_reverse(action) else 0.0 - score = abs(new_dx) + abs(new_dy) + reverse_penalty + visit_penalty + preferred_bonus + stuck_escape - ranked.append((score, action)) - if ranked: - action = min(ranked)[1] - self.last_action = action - return action - return WAIT if mask[WAIT] else int(_valid_indices(mask)[0]) - - def explore(self, obs: np.ndarray, mask: np.ndarray, difficulty: str) -> int: - if float(obs[9]) > 0.0: - return WAIT - pos = self._pos(obs, difficulty) - choices = [] - for action, (dx, dy) in MOVE_DELTAS.items(): - if action < len(mask) and mask[action]: - new_pos = (pos[0] + dx, pos[1] + dy) - score = self.visits.get(new_pos, 0) + (0.5 if self._is_reverse(action) else 0.0) - choices.append((score, action)) - if choices: - action = min(choices)[1] - self.last_action = action - return action - if HARVEST < len(mask) and mask[HARVEST]: - return HARVEST - return WAIT - - def _is_reverse(self, action: int) -> bool: - pairs = { - MOVE_UP: MOVE_DOWN, - MOVE_DOWN: MOVE_UP, - MOVE_LEFT: MOVE_RIGHT, - MOVE_RIGHT: MOVE_LEFT, - } - return self.last_action is not None and pairs.get(action) == self.last_action - - -def build_macro_observation( - obs: np.ndarray, - primitive_mask: np.ndarray, - difficulty: str = "medium", - memory: PriceMemory | None = None, -) -> np.ndarray: - features = np.zeros(MACRO_OBS_DIM, dtype=np.float32) - obs_arr = np.asarray(obs, dtype=np.float32) - mask_arr = np.asarray(primitive_mask, dtype=np.float32) - features[:OBS_DIM] = obs_arr[:OBS_DIM] - features[OBS_DIM : OBS_DIM + N_PRIMITIVE_ACTIONS] = mask_arr[:N_PRIMITIVE_ACTIONS] - base = 110 - for i, name in enumerate(DIFFICULTIES): - features[base + i] = 1.0 if difficulty == name else 0.0 - features[113] = np.clip(product_inventory(obs_arr), 0.0, 2.0) - features[114] = np.clip(total_inventory(obs_arr), 0.0, 2.0) - features[115] = np.clip(money_from_obs(obs_arr) / 250.0, 0.0, 4.0) - features[116] = np.clip(compute_from_obs(obs_arr) / 120.0, 0.0, 3.0) - features[117] = time_ratio(obs_arr) - adj = adjacent_market(obs_arr, difficulty) - features[118] = -1.0 if adj is None else float(adj) / 3.0 - - offset = 120 - for idx in range(4): - if idx < market_count(difficulty): - features[offset] = np.clip(market_distance(obs_arr, idx, difficulty) / 20.0, 0.0, 2.0) - features[offset + 1] = np.clip(best_visible_upside(obs_arr, idx, difficulty) / 100.0, -2.0, 2.0) - features[offset + 2] = np.clip(held_value_at(obs_arr, idx, difficulty) / 120.0, 0.0, 3.0) - if memory is not None: - trends = [memory.trend(idx, pid) for pid in range(5)] - features[offset + 3] = np.clip(max(trends) / 30.0, -2.0, 2.0) - features[offset + 4] = 1.0 if market_is_adjacent(obs_arr, idx, difficulty) else 0.0 - offset += 5 - - for i, action in enumerate(TECH_ACTIONS): - features[140 + i] = 1.0 if tech_owned(obs_arr, action) else 0.0 - hint = expert_macro_hint(obs_arr, primitive_mask, difficulty, memory) - if 0 <= hint < MACRO_ACTION_COUNT: - features[148 + hint] = 1.0 - return features - - -def build_macro_action_mask(obs: np.ndarray, primitive_mask: np.ndarray, difficulty: str = "medium") -> np.ndarray: - mask = np.zeros(MACRO_ACTION_COUNT, dtype=bool) - inv = product_inventory(obs) - params = params_for(difficulty) - count = market_count(difficulty) - mask[FOLLOW_EXPERT] = True - for idx in range(count): - mask[BUY_MARKET_0 + idx] = inv < params.buy_fill - mask[SELL_MARKET_0 + idx] = inv > 1e-6 - mask[FACTORY_PLAN] = True - mask[OCCUPY_NEAREST] = bool(primitive_mask[OCCUPY]) or time_ratio(obs) < params.occupy_until - mask[WAIT_FOR_PRICE] = inv > 1e-6 and time_ratio(obs) < params.wait_until_time - mask[ENDGAME_LIQUIDATE] = inv > 1e-6 - return mask - - -def expert_macro_hint( - obs: np.ndarray, - primitive_mask: np.ndarray, - difficulty: str, - memory: PriceMemory | None = None, -) -> int: - params = params_for(difficulty) - inv = product_inventory(obs) - if inv > 1e-6 and time_ratio(obs) >= params.endgame_time: - return ENDGAME_LIQUIDATE - if inv > 1e-6: - target = choose_sell_market(obs, difficulty) - return SELL_MARKET_0 + min(target, 3) - target = choose_buy_market(obs, difficulty) - return BUY_MARKET_0 + min(target, 3) - - -class HybridController: - def __init__(self): - self.navigator = OnlineNavigator() - self.price_memory = PriceMemory() - self.targets = TargetSelector() - self.current_option: int | None = None - self.option_steps = 0 - self.no_trade_steps = 0 - self.prev_signature: tuple[float, ...] | None = None - - def reset(self): - self.navigator.reset() - self.price_memory.reset() - self.current_option = None - self.option_steps = 0 - self.no_trade_steps = 0 - self.prev_signature = None - - def observe(self, obs: np.ndarray, difficulty: str): - self.navigator.observe(obs, difficulty) - self.price_memory.observe(obs, difficulty) - sig = tuple(np.round(np.asarray(obs[3:12], dtype=float), 4)) - if self.prev_signature is not None and sig == self.prev_signature: - self.no_trade_steps += 1 - else: - self.no_trade_steps = 0 - self.prev_signature = sig - - def start_option(self, macro_action: int): - self.current_option = int(macro_action) - self.option_steps = 0 - - def macro_observation(self, obs: np.ndarray, primitive_mask: np.ndarray, difficulty: str) -> np.ndarray: - return build_macro_observation(obs, primitive_mask, difficulty, self.price_memory) + for smid, sp in prices.items(): + if bmid == smid: + continue + sx, sy = self.mkts[smid] + if (sx, sy) in self._black: + continue + for pid in range(5): + buy_p = bp.get(pid, 0) + sell_p = sp.get(pid, 0) + if buy_p <= 0 or sell_p <= buy_p * 1.02: + continue + margin = sell_p - buy_p + # 综合评分：利润率 - 距离惩罚 + profit_rate = margin / max(buy_p, 1) + travel = abs(self.x - bx) + abs(self.y - by) + abs(bx - sx) + abs(by - sy) + # 有效的单步利润率（travel cost per unit is amortized over capacity） + cap = self._capacity(obs) + # 能买多少单位 + affordable = int(self.money / max(buy_p, 1)) if self.money > 0 else 0 + units = min(affordable, int(cap)) + if units <= 0: + continue + total_profit = units * margin + # 评分：单步期望收益（buy: units*2 tick, sell: 一次卖全部仅2 tick） + steps_est = travel + units * 2 + 2 + if steps_est <= 0: + continue + score = total_profit / steps_est + if score > best_score: + best_score = score + best = (bmid, smid, pid, margin, total_profit, buy_p, sell_p, units) - def macro_action_mask(self, obs: np.ndarray, primitive_mask: np.ndarray, difficulty: str) -> np.ndarray: - return build_macro_action_mask(obs, primitive_mask, difficulty) + return best - def expert_action(self, obs: np.ndarray, primitive_mask: np.ndarray, difficulty: str) -> int: - mask = np.asarray(primitive_mask, dtype=bool) - if float(obs[9]) > 0.0: - return WAIT - params = params_for(difficulty) - current_market = adjacent_market(obs, difficulty) - sell_action = best_sell_action(obs, mask, current_market, difficulty) - inv = product_inventory(obs) - - if inv > 1e-6 and time_ratio(obs) >= params.endgame_time: - if sell_action is not None: - return sell_action - return self._go_to_market(self.targets.sell_market(obs, difficulty), obs, mask, difficulty) - - if sell_action is not None and self._should_sell_now(obs, current_market, difficulty): - return sell_action - - factory_action = self._factory_action(obs, mask, difficulty) - if factory_action is not None: - return factory_action - - has_raw = float(obs[3]) > 0.05 - has_factory_stock = bool(np.sum(np.clip(obs[16:21], 0.0, 2.0)) > 1e-6) - if time_ratio(obs) < params.factory_until and (has_raw or has_factory_stock): - return self._factory_plan(obs, mask, difficulty) - - if inv < params.buy_fill and BUY < len(mask) and mask[BUY]: - return BUY - - if inv > 1e-6: - return self._go_to_market(self.targets.sell_market(obs, difficulty), obs, mask, difficulty) - - if HARVEST < len(mask) and mask[HARVEST] and (money_from_obs(obs) < 8.0 or time_ratio(obs) < 0.18): - return HARVEST - - if self.no_trade_steps > params.explore_after: - return self.navigator.explore(obs, mask, difficulty) - return self._go_to_market(self.targets.buy_market(obs, difficulty), obs, mask, difficulty) - - def primitive_for_macro( - self, - macro_action: int, - obs: np.ndarray, - primitive_mask: np.ndarray, - difficulty: str, - ) -> int: - self.option_steps += 1 - macro_action = int(macro_action) - if macro_action == FOLLOW_EXPERT: - return self.expert_action(obs, primitive_mask, difficulty) - if BUY_MARKET_0 <= macro_action <= BUY_MARKET_3: - return self._buy_at_market(macro_action - BUY_MARKET_0, obs, primitive_mask, difficulty) - if SELL_MARKET_0 <= macro_action <= SELL_MARKET_3: - return self._sell_at_market(macro_action - SELL_MARKET_0, obs, primitive_mask, difficulty) - if macro_action == FACTORY_PLAN: - return self._factory_plan(obs, primitive_mask, difficulty) - if macro_action == OCCUPY_NEAREST: - return self._occupy_plan(obs, primitive_mask, difficulty) - if macro_action == WAIT_FOR_PRICE: - if time_ratio(obs) > params_for(difficulty).wait_until_time: - return self.expert_action(obs, primitive_mask, difficulty) - return WAIT if primitive_mask[WAIT] else self.expert_action(obs, primitive_mask, difficulty) - if macro_action == ENDGAME_LIQUIDATE: - return self._sell_at_market(self.targets.sell_market(obs, difficulty), obs, primitive_mask, difficulty) - return self.expert_action(obs, primitive_mask, difficulty) - - def option_complete(self, macro_action: int, obs: np.ndarray, last_primitive: int, difficulty: str) -> bool: - if last_primitive in (BUY, LOAD, DEPOSIT, OCCUPY) or SELL_0 <= last_primitive <= SELL_4: - return True - if macro_action == WAIT_FOR_PRICE: - return self.option_steps >= 3 - if macro_action == FACTORY_PLAN: - return self.option_steps >= 12 - if macro_action == OCCUPY_NEAREST: - return self.option_steps >= 18 or bool(last_primitive == OCCUPY) - return self.option_steps >= self.option_limit(macro_action) - - def option_limit(self, macro_action: int) -> int: - if BUY_MARKET_0 <= macro_action <= SELL_MARKET_3: - return 18 - if macro_action == ENDGAME_LIQUIDATE: - return 24 - if macro_action == FACTORY_PLAN: - return 18 - return 8 - - def _should_sell_now(self, obs: np.ndarray, market: int | None, difficulty: str) -> bool: - if market is None: - return False - if time_ratio(obs) >= params_for(difficulty).wait_until_time: - return True - current = held_value_at(obs, market, difficulty) - best = max(held_value_at(obs, idx, difficulty) for idx in range(market_count(difficulty))) - trend_bonus = 0.0 - for pid in range(5): - if obs[4 + pid] > 1e-6: - trend_bonus = max(trend_bonus, self.price_memory.trend(market, pid)) - return current + params_for(difficulty).sell_margin >= best or trend_bonus < -1.5 - - def _go_to_market(self, index: int, obs: np.ndarray, mask: np.ndarray, difficulty: str) -> int: - index = int(np.clip(index, 0, market_count(difficulty) - 1)) - base = market_base(index) - return self.navigator.move_toward(obs, mask, float(obs[base]), float(obs[base + 1]), difficulty) - - def _buy_at_market(self, index: int, obs: np.ndarray, mask: np.ndarray, difficulty: str) -> int: - if index >= market_count(difficulty): - return self.expert_action(obs, mask, difficulty) - if market_is_adjacent(obs, index, difficulty) and mask[BUY] and product_inventory(obs) < params_for(difficulty).buy_fill: - return BUY - return self._go_to_market(index, obs, mask, difficulty) - - def _sell_at_market(self, index: int, obs: np.ndarray, mask: np.ndarray, difficulty: str) -> int: - if index >= market_count(difficulty): - index = self.targets.sell_market(obs, difficulty) - current = adjacent_market(obs, difficulty) - if current == index: - sell_action = best_sell_action(obs, mask, current, difficulty) - if sell_action is not None: - return sell_action - return self._go_to_market(index, obs, mask, difficulty) - - def _factory_action(self, obs: np.ndarray, mask: np.ndarray, difficulty: str) -> int | None: - if float(obs[0]) > 0.04 or float(obs[1]) > 0.04: - return None - params = params_for(difficulty) - if time_ratio(obs) > params.factory_until: - if LOAD < len(mask) and mask[LOAD]: - return LOAD + def _best_sell_market(self, obs, avoid_mid=None): + """为身上产品找到最佳卖出市场。""" + prod_inv = self._prod_inv(obs) + if not prod_inv: return None - if DEPOSIT < len(mask) and mask[DEPOSIT]: - return DEPOSIT - if LOAD < len(mask) and mask[LOAD] and product_inventory(obs) < params.buy_fill: - return LOAD - for tech in params.tech_priority: - if tech < len(mask) and mask[tech]: - return tech - produce = self._best_produce_action(obs, mask, difficulty) - if produce is not None: - return produce - return None - def _factory_plan(self, obs: np.ndarray, mask: np.ndarray, difficulty: str) -> int: - action = self._factory_action(obs, mask, difficulty) - if action is not None: - return action - return self.navigator.move_toward(obs, mask, -float(obs[0]), -float(obs[1]), difficulty) - - def _occupy_plan(self, obs: np.ndarray, mask: np.ndarray, difficulty: str) -> int: - if OCCUPY < len(mask) and mask[OCCUPY]: - return OCCUPY - center = self.targets.nearest_closed_compute_center(obs, difficulty) - if center is not None: - _, base = center - return self.navigator.move_toward(obs, mask, float(obs[base]), float(obs[base + 1]), difficulty) - return self.expert_action(obs, mask, difficulty) - - def _best_produce_action(self, obs: np.ndarray, mask: np.ndarray, difficulty: str) -> int | None: - best: tuple[float, int] | None = None - for action in PRODUCE_ACTIONS: - if action >= len(mask) or not mask[action]: - continue - pid = action - PRODUCE_0 - best_price = max(price_at(obs, market, pid, difficulty) for market in range(market_count(difficulty))) - score = best_price / max(1.0, float(RAW_COST[pid])) - if best is None or score > best[0]: - best = (score, action) - return None if best is None else best[1] - - -class PureArbitrageController: - def __init__(self, env: Any): - self.env = env - self.fallback = HybridController() - self.last_time = -1.0 - - def reset(self): - self.fallback.reset() - self.last_time = -1.0 - self._forecast = None - self._forecast_episode = None - - def action(self, obs: np.ndarray, mask: np.ndarray, difficulty: str) -> int: - self.fallback.observe(obs, difficulty) - if float(obs[9]) > 0.0: - return WAIT - if not self._has_world(): - return self.fallback.expert_action(obs, mask, difficulty) + prices = self._all_prices(obs) + best = None + best_total = 0 - env = self.env - unit = env.unit - current_time = time_ratio(obs) - - sell_action = self._best_adjacent_sell(mask) - force_sell_time = {"easy": 0.90, "medium": 0.90, "hard": 1.10}.get(difficulty, 0.90) - if sell_action is not None and (current_time > force_sell_time or unit.free_capacity <= 0): - return sell_action + for mid, mp in prices.items(): + if mid == avoid_mid: + continue + mx, my = self.mkts[mid] + if (mx, my) in self._black: + continue + total = 0 + sellable = 0 + for pid, qty in prod_inv.items(): + price = mp.get(pid, 0) + if price > 0: + total += qty * price + sellable += qty + if sellable > 0 and total > best_total: + best_total = total + d = abs(self.x - mx) + abs(self.y - my) + best = (mid, total, sellable, d) + + return best + + # ═══════════════════════════════════════════════════════════════════════════ + # 算力中心与科技 + # ═══════════════════════════════════════════════════════════════════════════ + + def _nearest_closed_center(self): + """返回最近的未占领算力中心。""" + best_cid = None + best_d = 999 + for cid, (cx, cy) in self.ctrs.items(): + if cid in self._occupied: + continue + if (cx, cy) in self._black: + continue + path = self._astar(cx, cy) + if path is None: + self._blacklist((cx, cy)) + continue + d = abs(self.x - cx) + abs(self.y - cy) + if d < best_d: + best_d = d + best_cid = cid + return best_cid + + def _next_tech_to_buy(self, obs): + """返回下一个应该购买的科技 TECH_x ID（mask 已检查），或 None。""" + techs = self._techs_owned(obs) + for key in TECH_PRIORITY: + tid = TECH_KEY_TO_ID[key] + idx = tid - TECH_0 + if techs[idx]: + continue # 已拥有 + # 检查失败次数（避免死循环） + fails = self._tech_attempted.get(key, 0) + if fails >= 5: + continue + return tid + return None + def _should_get_techs(self, obs, mask): + """判断是否应该去工厂买科技。""" + # 已经有 path_optimization → 不需要 + techs = self._techs_owned(obs) + if techs[5] and techs[3]: + return False + if self._tech_attempted.get("path_optimization", 0) >= 5: + return False + # 在工厂且 mask 允许科技购买 + if not self._at_factory(): + return False + tid = self._next_tech_to_buy(obs) + if tid is None or not mask[tid]: + return False + return True + + # ═══════════════════════════════════════════════════════════════════════════ + # 主策略 + # ═══════════════════════════════════════════════════════════════════════════ + + def _plan(self, obs, mask): + self._blacklist_maintain() + + techs = self._techs_owned(obs) + has_prod = self._has_prod(obs) + prod_inv = self._prod_inv(obs) + prod_qty = sum(prod_inv.values()) + has_path_opt = techs[5] + cap = self._capacity(obs) + + # 站哪个市场旁？ + at_mid = self._which_market_adjacent() + + # ═══════════════════════════════════════════════════════════════════════ + # P0: 在买入市场 → 继续批量买入（直到满了或没钱了再走） + # ═══════════════════════════════════════════════════════════════════════ + if has_prod and at_mid is not None and at_mid == self._last_buy_mid: + # 在买入市场，检查能否继续买 + if mask[BUY] and prod_qty < cap and self.money >= 1.0: + self._state = "ARB_BUY" + self._set_goal(None, None, BUY) + return + + # ═══════════════════════════════════════════════════════════════════════ + # P1: 在非买入市场旁 → 卖出手持产品 + # ═══════════════════════════════════════════════════════════════════════ + if has_prod and at_mid is not None and at_mid != self._last_buy_mid: + for pid in list(prod_inv.keys()): + if mask[SELL_0 + pid]: + self._state = "SELL" + self._set_goal(None, None, SELL_0 + pid) + return + best_sell = self._best_sell_market(obs, avoid_mid=self._last_buy_mid) + if best_sell: + self._state = "GO_SELL" + self._set_goal(*self.mkts[best_sell[0]], None) + return + + # ═══════════════════════════════════════════════════════════════════════ + # P1.5: 在工厂 → 买科技或等待算力 + # ═══════════════════════════════════════════════════════════════════════ if self._at_factory(): - tech = self._factory_tech(mask, difficulty, current_time) - if tech is not None: - return tech - if LOAD < len(mask) and mask[LOAD] and unit.free_capacity > 0: - return LOAD - if DEPOSIT < len(mask) and mask[DEPOSIT]: - return DEPOSIT - produce = self._produce_action(mask) - if produce is not None and current_time < 0.72: - return produce - - refill_until = {"easy": 0.995, "medium": 0.990, "hard": 0.99}.get(difficulty, 0.90) - if self._carrying_products() and unit.free_capacity > self._min_free_before_sell(difficulty) and current_time < refill_until: - buy_plan = self._best_buy_market(difficulty) - if buy_plan is not None and buy_plan[1] > 0.25: - market_id, _profit = buy_plan - if self._adjacent_market_id() == market_id and BUY < len(mask) and mask[BUY]: - return BUY - action = self._move_to_market(market_id, mask) - if action is not None: - return action - - if ( - difficulty == "hard" - and current_time < 0.55 - and float(getattr(env, "compute", 0.0)) >= 80.0 - and abs(int(env.unit.x) - int(env.cfg.factory_x)) + abs(int(env.unit.y) - int(env.cfg.factory_y)) <= 15 - and not self._at_factory() - ): - action = self._move_to_factory(mask) - if action is not None: - return action - - if ( - difficulty == "easy" - and current_time < 0.90 - and float(getattr(env, "compute", 0.0)) >= 80.0 - and abs(int(env.unit.x) - int(env.cfg.factory_x)) + abs(int(env.unit.y) - int(env.cfg.factory_y)) <= 6 - and not self._at_factory() - ): - action = self._move_to_factory(mask) - if action is not None: - return action - - if ( - difficulty == "medium" - and current_time < 0.90 - and float(getattr(env, "compute", 0.0)) >= 80.0 - and abs(int(env.unit.x) - int(env.cfg.factory_x)) + abs(int(env.unit.y) - int(env.cfg.factory_y)) <= 8 - and not self._at_factory() - ): - action = self._move_to_factory(mask) - if action is not None: - return action - - if self._carrying_products(): - if sell_action is not None and self._should_sell_here(obs, difficulty): - return sell_action - target = self._best_sell_market(difficulty) - if target is not None: - action = self._move_to_market(target, mask) - if action is not None: - return action - - if self._carrying_raw(): - action = self._move_to_factory(mask) - if action is not None: - return action - - if ( - difficulty == "hard" - and current_time < 0.55 - and float(getattr(env, "compute", 0.0)) >= 80.0 - and not self._carrying_products() - and not self._carrying_raw() - and not self._at_factory() - ): - action = self._move_to_factory(mask) - if action is not None: - return action - - if ( - difficulty == "hard" - and current_time < 0.30 - and float(getattr(env, "compute", 0.0)) >= 30.0 - and not self._carrying_products() - and not self._carrying_raw() - and not self._at_factory() - ): - action = self._move_to_factory(mask) - if action is not None: - return action - - if ( - difficulty == "medium" - and current_time < 0.90 - and float(getattr(env, "compute", 0.0)) >= 80.0 - and abs(int(env.unit.x) - int(env.cfg.factory_x)) + abs(int(env.unit.y) - int(env.cfg.factory_y)) <= 8 - and not self._at_factory() - ): - action = self._move_to_factory(mask) - if action is not None: - return action - - if self._factory_has_products() and unit.free_capacity > 2 and current_time < 0.86: - action = self._move_to_factory(mask) - if action is not None: - return action - - if self._factory_can_produce() and current_time < 0.72: - action = self._move_to_factory(mask) - if action is not None: - return action - - if ( - (difficulty == "easy" and current_time < 0.10) - or (difficulty == "medium" and current_time < 0.15) - or (difficulty == "hard" and current_time < 0.06) - ): - occupy = self._compute_center_plan(mask) - if occupy is not None: - return occupy - - if unit.free_capacity <= 1: - target = self._best_sell_market(difficulty) - if target is not None: - action = self._move_to_market(target, mask) - if action is not None: - return action - - buy_until = {"easy": 0.98, "medium": 0.99, "hard": 0.970}.get(difficulty, 0.95) - if unit.free_capacity > 1 and current_time < buy_until: - buy_plan = self._best_buy_market(difficulty) - if buy_plan is not None and (buy_plan[1] >= self._trade_profit_floor(difficulty) or current_time > 0.55): - market_id, _profit = buy_plan - if self._adjacent_market_id() == market_id and BUY < len(mask) and mask[BUY]: - return BUY - action = self._move_to_market(market_id, mask) - if action is not None: - return action - - resource = self._resource_plan(mask, difficulty, current_time) - if resource is not None: - return resource - - target = self._best_sell_market(difficulty) if self._carrying_products() else None - if target is not None: - action = self._move_to_market(target, mask) - if action is not None: - return action - return self.fallback.expert_action(obs, mask, difficulty) - - def _has_world(self) -> bool: - return all(hasattr(self.env, name) for name in ("unit", "board", "markets", "money", "factory")) - - def _at_factory(self) -> bool: - return bool(self.env.board.at_factory(self.env.unit.x, self.env.unit.y)) - - def _carrying_products(self) -> bool: - return any(qty > 1e-9 for qty in self.env.unit.prod_inv.values()) - - def _carrying_raw(self) -> bool: - return float(getattr(self.env.unit, "raw_inv", 0.0)) > 1e-9 - - def _factory_has_products(self) -> bool: - return float(getattr(self.env.factory, "total_product_stock", 0.0)) > 1e-9 - - def _factory_can_produce(self) -> bool: - raw = float(getattr(self.env.factory, "raw_stock", 0.0)) - return any(raw >= float(cost) for cost in RAW_COST) - - def _price_multiplier(self) -> float: - try: - return float(self.env.factory.price_multiplier) - except Exception: - return 1.0 - - def _effective_buy_price(self, market: Any, pid: int) -> float: - return max(0.0, float(market.get_price(pid)) + float(getattr(self.env.factory, "cost_delta", 0.0))) - - def _ensure_price_forecast(self): - if getattr(self, "_forecast_episode", None) == id(self.env.markets) and getattr(self, "_forecast", None) is not None: - return - try: - import random as _random - dt = float(self.env.cfg.time_step) - steps = int(getattr(self.env.cfg, "max_steps", 2000)) + 200 - markets = self.env.markets - prices = [[float(m._current_prices[pid]) for pid in range(5)] for m in markets] - rngs = [m._rng.getstate() for m in markets] - params = [m._ou_params for m in markets] - forecast = [] - for _ in range(steps + 1): - forecast.append([row[:] for row in prices]) - for mi in range(len(markets)): - rng = _random.Random(); rng.setstate(rngs[mi]) - for pid in range(5): - p = params[mi][pid] - cur = prices[mi][pid] - drift = 0.05 * (p["mean"] - cur) * dt - noise = rng.gauss(0, p["sigma"] * (dt ** 0.5)) - prices[mi][pid] = max(p["lo"], min(p["hi"], cur + drift + noise)) - rngs[mi] = rng.getstate() - self._forecast = forecast - self._forecast_episode = id(self.env.markets) - except Exception: - self._forecast = None - self._forecast_episode = id(self.env.markets) - - def _forecast_price(self, market_id: int, pid: int, tick: int) -> float: - forecast = getattr(self, "_forecast", None) - if forecast is None: - return float(self.env.markets[market_id].get_price(pid)) - idx = max(0, min(int(tick), len(forecast) - 1)) - return float(forecast[idx][market_id][pid]) - - def _move_ticks(self, dist: int) -> int: - return int(max(0, dist) if "path_optimization" in getattr(self.env, "_techs_owned", set()) else 2 * max(0, dist)) - - def install_buy_policy(self, difficulty: str): - if difficulty not in ("easy", "medium", "hard") or not self._has_world(): + tid = self._next_tech_to_buy(obs) + if tid is not None and mask[tid]: + self._state = "TECH" + self._set_goal(None, None, tid) + return + # 还需要科技但算力不够→在工厂等待算力积累（避免第二趟往返） + if tid is not None and self._occupied and not has_prod: + self._state = "WAIT_COMPUTE" + self._goal = None + self._goal_act = None + return + + # ═══════════════════════════════════════════════════════════════════════ + # P2: 需要核心科技且有足够算力 → 回工厂（仅一次） + # ═══════════════════════════════════════════════════════════════════════ + need_tech = (not techs[1]) or (not techs[5]) or (not techs[3]) + if need_tech and self.compute >= 90 and not self._at_factory(): + path = self._astar(0, 0) + if path is not None and len(path) <= 20: + self._state = "GO_FACTORY" + self._set_goal(0, 0, None) + return + + # ═══════════════════════════════════════════════════════════════════════ + # P3: 有产品但不在市场 → 去最佳卖出市场 + # ═══════════════════════════════════════════════════════════════════════ + if has_prod and at_mid is None: + avoid = self._last_buy_mid if self._has_bought else None + best_sell = self._best_sell_market(obs, avoid_mid=avoid) + if best_sell: + self._state = "GO_SELL" + self._set_goal(*self.mkts[best_sell[0]], None) + return + + # ═══════════════════════════════════════════════════════════════════════ + # P3: 在市场旁且空手 → 开始批量买入 + # ═══════════════════════════════════════════════════════════════════════ + if not has_prod and at_mid is not None and mask[BUY] and self.money >= 1.0: + self._state = "ARB_BUY" + self._last_buy_mid = at_mid + self._has_bought = True + self._set_goal(None, None, BUY) return - env = self.env - self._ensure_price_forecast() - - def best_buyable(mkt: Any): - best_pid = None - best_price = None - best_score = -1e18 - mid = int(mkt.id) - now = int(getattr(env, "_step", 0)) - mult = self._price_multiplier() - fill = int(max(1, min(float(env.unit.free_capacity), 35.0))) - buy_done_tick = now + 2 * fill - product_ids = (0, 3) if difficulty == "easy" else (((0, 1, 3) if float(getattr(self.env, "money", 0.0)) < 3000.0 else (0,)) if difficulty == "medium" else (0, 1, 3)) - for pid in product_ids: - effective = max(0.0, float(mkt.get_price(pid)) + float(getattr(env.factory, "cost_delta", 0.0))) - if float(env.money) < effective: + + # ═══════════════════════════════════════════════════════════════════════ + # P4: 未占领任何中心 → 主动去最近的计算中心（开局最关键！） + # ═══════════════════════════════════════════════════════════════════════ + if not self._occupied and self.ctrs and not has_prod: + cid = self._nearest_closed_center() + if cid is not None: + path = self._astar(*self.ctrs[cid]) + if path is not None: + self._state = "GO_CTR" + self._set_goal(*self.ctrs[cid], None) + return + + # ═══════════════════════════════════════════════════════════════════════ + # P6: 空手 → 找最佳套利对 → 去买入市场 + # ═══════════════════════════════════════════════════════════════════════ + if not has_prod and len(self.mkts) >= 2: + arb = self._best_arbitrage(obs) + if arb and arb[4] > 2: # 降低门槛，微小利润也能积累 + bmid = arb[0] + bx, by = self.mkts[bmid] + self._state = "ARB_GO_BUY" + self._set_goal(bx, by, None) + return + + # ═══════════════════════════════════════════════════════════════════════ + # P7: 有产品但没找到卖点 → 去最近非买入市场（兜底） + # ═══════════════════════════════════════════════════════════════════════ + if has_prod and self.mkts: + best_mid = None + best_d = 999 + for mid, (mx, my) in self.mkts.items(): + if mid == self._last_buy_mid: continue - best_sell = 0.0 - for other in env.markets: - oid = int(other.id) - if oid == mid: - continue - dist = abs(int(other.x) - int(mkt.x)) + abs(int(other.y) - int(mkt.y)) - tick = buy_done_tick + self._move_ticks(dist) - best_sell = max(best_sell, self._forecast_price(oid, pid, tick) * mult) - profit = best_sell - effective - if profit <= (0.0 if difficulty == "hard" else 0.5): + if (mx, my) in self._black: + continue + d = abs(self.x - mx) + abs(self.y - my) + if d < best_d: + best_d = d + best_mid = mid + if best_mid is not None: + self._state = "GO_SELL" + self._set_goal(*self.mkts[best_mid], None) + return + + # ═══════════════════════════════════════════════════════════════════════ + # P8: 探索/去最近市场 + # ═══════════════════════════════════════════════════════════════════════ + if self.mkts: + best_mid = None + best_score = -999 + for mid, (mx, my) in self.mkts.items(): + if (mx, my) in self._black: continue - score = self._buy_unit_score(profit, best_sell, difficulty) + d = max(1, abs(self.x - mx) + abs(self.y - my)) + score = 50.0 / d if score > best_score: best_score = score - best_pid = pid - best_price = effective - return best_pid, best_price - - env._best_buyable = best_buyable - - def _buy_unit_score(self, profit: float, sell: float, difficulty: str) -> float: - if difficulty == "easy" and float(getattr(self.env, "money", 0.0)) >= 0.0: - return float(profit) + 1.00 * float(sell) - if difficulty == "medium" and float(getattr(self.env, "money", 0.0)) >= 300.0: - return float(profit) + 0.80 * float(sell) - if difficulty == "hard" and float(getattr(self.env, "money", 0.0)) >= 500.0: - return float(profit) + 0.80 * float(sell) - return float(profit) - - def _best_adjacent_sell(self, mask: np.ndarray) -> int | None: - market_id = self._adjacent_market_id() - if market_id is None: - return None - market = self.env.markets[market_id] - best: tuple[float, int] | None = None - for action in SELL_ACTIONS: - if action >= len(mask) or not bool(mask[action]): - continue - pid = action - SELL_0 - qty = max(0.0, float(self.env.unit.prod_inv.get(pid, 0.0)) - float(self.env.unit.origin_qty(pid, market_id))) - if qty <= 1e-9: - continue - value = qty * float(market.get_price(pid, self._price_multiplier())) - if best is None or value > best[0]: - best = (value, action) - return None if best is None else best[1] - - def _should_sell_here(self, obs: np.ndarray, difficulty: str) -> bool: - here = self._adjacent_market_id() - if here is None: - return False - here_value = self._sell_value_at(here) - best = max((self._sell_value_at(i) for i in range(len(self.env.markets))), default=0.0) - slack = {"easy": 0.20, "medium": 0.35, "hard": 0.40}.get(difficulty, 0.35) - if time_ratio(obs) > 0.82: - slack -= 0.08 - return here_value >= best * slack - - def _sell_value_at(self, market_id: int) -> float: - market = self.env.markets[market_id] - value = 0.0 - for pid, qty_raw in self.env.unit.prod_inv.items(): - qty = max(0.0, float(qty_raw) - float(self.env.unit.origin_qty(pid, market_id))) - if qty > 0.0: - value += qty * float(market.get_price(pid, self._price_multiplier())) - return value - - def _best_sell_market(self, difficulty: str) -> int | None: - best: tuple[float, int] | None = None - now = int(getattr(self.env, "_step", 0)) - mult = self._price_multiplier() - for idx in range(len(self.env.markets)): - dist = self._distance_to_market(idx) - tick = now + self._move_ticks(dist) - value = 0.0 - for pid, qty_raw in self.env.unit.prod_inv.items(): - qty = max(0.0, float(qty_raw) - float(self.env.unit.origin_qty(pid, idx))) - if qty > 0.0: - value += qty * self._forecast_price(idx, pid, tick) * mult - if value <= 0.0: - continue - sell_dist_penalty = 0.0 if difficulty == "medium" else 1.0 - score = value - sell_dist_penalty * dist - if best is None or score > best[0]: - best = (score, idx) - return None if best is None else best[1] - - def _best_buy_market(self, difficulty: str) -> tuple[int, float] | None: - best: tuple[float, int, float] | None = None - money = float(self.env.money) - mult = self._price_multiplier() - for idx, market in enumerate(self.env.markets): - local_best = 0.0 - product_ids = (0, 3) if difficulty == "easy" else (((0, 1, 3) if float(getattr(self.env, "money", 0.0)) < 3000.0 else (0,)) if difficulty == "medium" else (0, 1, 3)) - for pid in product_ids: - buy = self._effective_buy_price(market, pid) - if money < buy: - continue - now = int(getattr(self.env, "_step", 0)) - fill = int(max(1, min(float(self.env.unit.free_capacity), 35.0))) - sell = 0.0 - for other in self.env.markets: - if int(other.id) == int(market.id): - continue - dist2 = abs(int(other.x) - int(market.x)) + abs(int(other.y) - int(market.y)) - tick = now + self._move_ticks(self._distance_to_market(idx)) + 2 * fill + self._move_ticks(dist2) - sell = max(sell, self._forecast_price(int(other.id), pid, tick) * mult) - profit = sell - buy - if profit > (0.0 if difficulty == "hard" else 0.5): - local_best = max(local_best, self._buy_unit_score(profit, sell, difficulty)) - if local_best <= 0.5: - continue - dist = self._distance_to_market(idx) - dist_penalty = {"easy": 160.0, "medium": 280.0, "hard": 320.0}.get(difficulty, 120.0) - load_cap = {"easy": 30.0, "medium": 30.0, "hard": 35.0}.get(difficulty, 30.0) - score = local_best * max(1.0, min(float(self.env.unit.free_capacity), load_cap)) - dist_penalty * dist - if best is None or score > best[0]: - best = (score, idx, local_best) - if best is None: - return None - return best[1], best[2] - - def _trade_profit_floor(self, difficulty: str) -> float: - return {"easy": 6.0, "medium": 10.0, "hard": 12.0}.get(difficulty, 10.0) - - def _min_free_before_sell(self, difficulty: str) -> int: - return {"easy": 2, "medium": 1, "hard": 1}.get(difficulty, 2) - - def _factory_tech(self, mask: np.ndarray, difficulty: str, current_time: float) -> int | None: - tech_until = {"easy": 0.90, "medium": 0.90, "hard": 0.55}.get(difficulty, 0.30) - if current_time > tech_until: - return None - priorities = { - "easy": (TECH_0 + 3, TECH_0 + 2, TECH_0 + 1, TECH_0 + 5, TECH_0 + 0), - "medium": (TECH_0 + 3, TECH_0 + 2, TECH_0 + 1, TECH_0 + 5, TECH_0 + 0, TECH_0 + 6), - "hard": (TECH_0 + 1, TECH_0 + 5, TECH_0 + 2, TECH_0 + 3, TECH_0 + 0), - }.get(difficulty, (TECH_0 + 3, TECH_0 + 2)) - for action in priorities: - if action < len(mask) and bool(mask[action]): - return action - return None + best_mid = mid + if best_mid is not None: + self._state = "GO_MKT" + self._set_goal(*self.mkts[best_mid], None) + return + + # ═══════════════════════════════════════════════════════════════════════ + # P11: 探索未知区域 + # ═══════════════════════════════════════════════════════════════════════ + ep = self._explore() + if ep: + self._state = "EXPLORE" + self._set_goal(*ep, None) + return - def _produce_action(self, mask: np.ndarray) -> int | None: - best: tuple[float, int] | None = None - mult = self._price_multiplier() - for action in PRODUCE_ACTIONS: - if action >= len(mask) or not bool(mask[action]): - continue - pid = action - PRODUCE_0 - value = max(float(m.get_price(pid, mult)) for m in self.env.markets) / max(1.0, float(RAW_COST[pid])) - if best is None or value > best[0]: - best = (value, action) - return None if best is None else best[1] - - def _resource_plan(self, mask: np.ndarray, difficulty: str, current_time: float) -> int | None: - if current_time > {"easy": 0.55, "medium": 0.62, "hard": 0.68}.get(difficulty, 0.60): - return None - if HARVEST < len(mask) and mask[HARVEST] and self.env.unit.free_capacity > 1: - return HARVEST - resources = [ - rp for rp in self.env.board.resource_points - if float(getattr(rp, "stock", 0.0)) > 1.0 - ] - if not resources or self.env.unit.free_capacity <= 2: - return None - rp = min(resources, key=lambda r: abs(int(r.x) - int(self.env.unit.x)) + abs(int(r.y) - int(self.env.unit.y))) - return self._move_to_adjacency(int(rp.x), int(rp.y), 2, mask) - - def _compute_center_plan(self, mask: np.ndarray) -> int | None: - if OCCUPY < len(mask) and bool(mask[OCCUPY]): - return OCCUPY - candidates = [cc for cc in self.env.board.compute_centers if not bool(cc.is_open)] - if not candidates: - return None - cc = min(candidates, key=lambda c: abs(c.x - self.env.unit.x) + abs(c.y - self.env.unit.y)) - return self._move_to_adjacency(cc.x, cc.y, 1, mask) + self._state = "IDLE" + self._goal = None - def _adjacent_market_id(self) -> int | None: - pos = self.env.board.nearest_market(self.env.unit.x, self.env.unit.y) - if pos is None: - return None - market = self.env.market_at(*pos) - return None if market is None else int(market.id) - - def _distance_to_market(self, market_id: int) -> int: - m = self.env.markets[market_id] - return max(0, abs(int(m.x) - int(self.env.unit.x)) + abs(int(m.y) - int(self.env.unit.y))) - - def _move_to_market(self, market_id: int, mask: np.ndarray) -> int | None: - market = self.env.markets[market_id] - return self._move_to_exact(int(market.x), int(market.y), mask) - - def _move_to_factory(self, mask: np.ndarray) -> int | None: - return self._move_to_exact(int(self.env.cfg.factory_x), int(self.env.cfg.factory_y), mask) - - def _move_to_adjacency(self, x: int, y: int, radius: int, mask: np.ndarray) -> int | None: - targets = set() - board = self.env.board - for i in range(board.H): - for j in range(board.W): - if abs(i - x) + abs(j - y) <= radius and board.is_passable(i, j): - targets.add((i, j)) - return self._bfs_first_action(targets, mask) - - def _move_to_exact(self, x: int, y: int, mask: np.ndarray) -> int | None: - return self._bfs_first_action({(x, y)}, mask) - - def _bfs_first_action(self, targets: set[tuple[int, int]], mask: np.ndarray) -> int | None: - start = (int(self.env.unit.x), int(self.env.unit.y)) - if start in targets: - return None - board = self.env.board - queue: list[tuple[int, int]] = [start] - parent: dict[tuple[int, int], tuple[tuple[int, int], int] | None] = {start: None} - head = 0 - while head < len(queue): - pos = queue[head] - head += 1 - for action, (dx, dy) in MOVE_DELTAS.items(): - nxt = (pos[0] + dx, pos[1] + dy) - if nxt in parent or not board.is_passable(nxt[0], nxt[1]): - continue - parent[nxt] = (pos, action) - if nxt in targets: - cur = nxt - first = action - while parent[cur] is not None: - prev, step_action = parent[cur] - if prev == start: - first = step_action - break - cur = prev - if first < len(mask) and bool(mask[first]): - return first - return None - queue.append(nxt) + def _which_market_adjacent(self): + """返回当前相邻的市场 id。""" + for mid, (mx, my) in self.mkts.items(): + if abs(self.x - mx) + abs(self.y - my) <= 1: + return mid return None - -class Agent(BaseAgent): - def __init__(self, env: Any, models: dict[str, Any] | None = None, use_models: bool = True): - super().__init__(env) - self.models = models or {} - self.use_models = bool(use_models) - self.router = DifficultyRouter(self.models) - self.safety = SafetyLayer(env) - self.pure = PureArbitrageController(env) - self.controllers = {name: HybridController() for name in DIFFICULTIES} - self.locked_difficulty: str | None = None - self.last_time = -1.0 - - def _maybe_reset_episode(self, obs: np.ndarray): - current_time = time_ratio(obs) - if current_time < self.last_time - 1e-6 or current_time <= 1e-8: - self.locked_difficulty = self.router.infer(obs) - self.pure.reset() - for controller in self.controllers.values(): - controller.reset() - elif self.locked_difficulty is None: - self.locked_difficulty = self.router.infer(obs) - self.last_time = current_time - - def get_action(self, observation: np.ndarray) -> int: - obs = np.asarray(observation, dtype=np.float32) - self._maybe_reset_episode(obs) - difficulty = self.locked_difficulty or self.router.infer(obs) - controller = self.controllers[difficulty] - self.pure.install_buy_policy(difficulty) - mask = self.safety.mask() - controller.observe(obs, difficulty) - - valid = self.safety.valid_indices(mask) - if len(valid) == 0: + # ═══════════════════════════════════════════════════════════════════════════ + # 主入口 + # ═══════════════════════════════════════════════════════════════════════════ + + def get_action(self, observation): + mask = self.env.action_masks() + + if self.mode == "rl" and self.policy_net is not None: + self._read_obs(observation) + self._check_stuck() + if self._busy(observation): + return WAIT + device = next(self.policy_net.parameters()).device + ot = torch.tensor(observation, dtype=torch.float32, device=device).unsqueeze(0) + mt = torch.tensor(mask, dtype=torch.bool, device=device).unsqueeze(0) + with torch.no_grad(): + return int(self.policy_net(ot, mt).argmax(dim=1).item()) + + # 规则模式 + valid = np.where(mask)[0] + self._read_obs(observation) + self._check_stuck() + + if self._busy(observation): return WAIT - primitive = self.pure.action(obs, mask, difficulty) - return self.safety.ensure_valid(primitive, mask, fallback=int(valid[0])) - - model = self.router.model_for(difficulty) if self.use_models else None - if model is None: - primitive = controller.expert_action(obs, mask, difficulty) - return self.safety.ensure_valid(primitive, mask, fallback=int(valid[0])) - - macro_action = FOLLOW_EXPERT - macro_action = self._predict_macro(model, controller, obs, mask, difficulty) - if controller.current_option is None: - controller.start_option(macro_action) - primitive = controller.primitive_for_macro(controller.current_option, obs, mask, difficulty) - if primitive < 0 or primitive >= len(mask) or not bool(mask[primitive]): - primitive = controller.expert_action(obs, mask, difficulty) - if controller.option_complete(controller.current_option, obs, primitive, difficulty): - controller.current_option = None - return self.safety.ensure_valid(primitive, mask, fallback=int(valid[0])) - - def _predict_macro(self, model: Any, controller: HybridController, obs: np.ndarray, mask: np.ndarray, difficulty: str) -> int: - macro_obs = controller.macro_observation(obs, mask, difficulty) - macro_mask = controller.macro_action_mask(obs, mask, difficulty) - try: - action, _ = model.predict(macro_obs, deterministic=True, action_masks=macro_mask) - action_int = int(action) - if 0 <= action_int < MACRO_ACTION_COUNT and macro_mask[action_int]: - if product_inventory(obs) <= 1e-6 and action_int in (WAIT_FOR_PRICE, ENDGAME_LIQUIDATE): - return FOLLOW_EXPERT - return action_int - except Exception: - self.router.drop_model(difficulty) - return FOLLOW_EXPERT - - def train(self, total_timesteps: int, **kwargs) -> dict[str, Any]: - return {"total_timesteps": int(total_timesteps), "trained": False} - - def save(self, path: str): - payload = self._metadata(models=[]) - path_obj = Path(path) - path_obj.parent.mkdir(parents=True, exist_ok=True) - with zipfile.ZipFile(path_obj, "w", compression=zipfile.ZIP_DEFLATED) as bundle: - bundle.writestr("metadata.json", json.dumps(payload, ensure_ascii=False, indent=2)) - - @staticmethod - def _metadata(models: list[str]) -> dict[str, Any]: - payload = { - "format": BUNDLE_FORMAT, - "selected_models": list(models), - } - if not models: - payload["note"] = "Missing difficulty models fall back to Expert." - return payload - - @staticmethod - def _load_maskable_ppo(model_path: Path) -> Any | None: - try: - from sb3_contrib import MaskablePPO - - model = MaskablePPO.load(model_path, device="auto") - if getattr(model.observation_space, "shape", None) == (MACRO_OBS_DIM,): - return model - except Exception: - return None - return None + # 只在非 busy 时学习障碍物 + self._learn_obs(mask) + + # ══ OCCUPY：最多占2个中心加速算力 ══ + if mask[OCCUPY] and len(self._occupied) < 2: + self._state = "OCCUPY" + self._set_goal(None, None, OCCUPY) + self._replan = False + self._goal_act = OCCUPY + + # 需要 replan？ + if self._goal_act is not None: + if not mask[self._goal_act]: + # 动作不再可用 + key = None + for k, v in TECH_KEY_TO_ID.items(): + if v == self._goal_act: + key = k + break + if key: + self._tech_attempted[key] = self._tech_attempted.get(key, 0) + 1 + self._plan(observation, mask) + elif self._replan or self._goal is None: + self._plan(observation, mask) + elif self._goal is not None and (self.x, self.y) == self._goal: + self._plan(observation, mask) + + # 执行动作 + if self._goal_act is not None and mask[self._goal_act]: + a = self._goal_act + self._goal_act = None + self._replan = True + elif self._goal is not None: + a = self._move_to(mask) + if a is None: + if (self.x, self.y) == self._goal: + # 到达目标但没本地动作 → replan + pass + self._goal = None + self._replan = True + a = (list(set(valid) & MOVE_SET) or [WAIT])[0] + else: + # 无目标，尝试移动或等待 + a = (list(set(valid) & MOVE_SET) or [WAIT])[0] + + if a is None: + a = WAIT + self._cells.add((self.x, self.y)) + return a + + # ═══════════════════════════════════════════════════════════════════════════ + # 训练 + 持久化 + # ═══════════════════════════════════════════════════════════════════════════ + + def train(self, total_timesteps=50000, save_dir="submission/", eval_freq=4096, **kwargs): + os.makedirs(save_dir, exist_ok=True) + scores = [] + best = -float("inf") + obs = self.reset() + for t in range(total_timesteps): + a = self.get_action(obs) + obs, r, term, trunc, info = self.step(a) + if term or trunc: + scores.append(info.get("score", 0)) + obs = self.reset() + if (t + 1) % eval_freq == 0 and len(scores) >= 3: + m = np.mean(scores[-3:]) + if m > best: + best = m + self.save(os.path.join(save_dir, "model_best.pt")) + self.save(os.path.join(save_dir, "model_final.pt")) + return {"total_timesteps": total_timesteps, "episodes": len(scores), + "mean_score_last10": np.mean(scores[-10:]) if scores else 0, + "best_mean_score": best} + + def save(self, path): + d = {"mode": self.mode} + if self.policy_net is not None: + d["policy_net"] = self.policy_net.state_dict() + torch.save(d, path) @classmethod - def load(cls, path: str, env: Any) -> "Agent": - models: dict[str, Any] = {} - path_obj = Path(path) - if path_obj.exists() and zipfile.is_zipfile(path_obj): - try: - with zipfile.ZipFile(path_obj, "r") as bundle: - names = set(bundle.namelist()) - bundle_members = {f"{difficulty}_model.zip" for difficulty in DIFFICULTIES} - if names & bundle_members: - with tempfile.TemporaryDirectory() as tmp: - tmp_dir = Path(tmp) - for difficulty in DIFFICULTIES: - member = f"{difficulty}_model.zip" - if member not in names: - continue - out_path = tmp_dir / member - out_path.write_bytes(bundle.read(member)) - model = cls._load_maskable_ppo(out_path) - if model is not None: - models[difficulty] = model - elif path_obj.suffix == ".zip": - model = cls._load_maskable_ppo(path_obj) - if model is not None: - models = {difficulty: model for difficulty in DIFFICULTIES} - except Exception: - models = {} - elif path_obj.exists() and path_obj.suffix == ".zip": - model = cls._load_maskable_ppo(path_obj) - if model is not None: - models = {difficulty: model for difficulty in DIFFICULTIES} - return cls(env=env, models=models, use_models=bool(models)) + def load(cls, path, env): + d = torch.load(path, map_location="cpu", weights_only=False) + a = cls(env, mode=d.get("mode", "rule")) + if "policy_net" in d: + a.policy_net = PolicyNet() + a.policy_net.load_state_dict(d["policy_net"]) + a.policy_net.eval() + a.policy_net.to("cuda" if torch.cuda.is_available() else "cpu") + a.mode = "rl" + return a diff --git a/logic/pve/submission/model.pt b/logic/pve/submission/model.pt index da3cc09fb2de9bc5754f7c05444a81c9db970fa2..7b1fa412a438154e6c868792244e701e5b9a7de4 100644 GIT binary patch literal 1249 zcmWIWW@cev;NW1u0DKH047vF!sX6*7i6x181=%@nP67;3XrO_qwvZtLXarboAtRVs zRGO1o$W*Ks;LXmVGTHOJEKnMR18`a<%Af$XOfN0JC^xYrzAUwCU=xWJeTWcS5+>7j)EFT-F0ivUoP(l1Ue$^^zhRbmM+qT@4D+?)(Zc8Y)h7diQfqUrHj#Fj6hLDac8HAH|d) zVAg;*1fKNJH6zC%FN)@CaLv#(gRT`hU}aFW1_1*Z8UR?MJiwccO$Vw&j#(G37nF!W n07kcivNjO>07n`q`qoMN3N_~>20jQ_vaGb)5!)U3SW{2|(ZOE>TH6>~2x@+!Zw zA#$c-a+%I^Pp(b1e?NZV_LaH*+ac}Bx_}j7r{@0UtmiT{JgoZEddGnsHUYKLZQt1g lycwB97;rlb=r%BDU<6Sp&J6HoWdjK^0--gKo(|$L004F+N>Ts- diff --git a/logic/pve/tests/test_official_evaluator_rules.py b/logic/pve/tests/test_official_evaluator_rules.py index e44aa743..e36041bd 100644 --- a/logic/pve/tests/test_official_evaluator_rules.py +++ b/logic/pve/tests/test_official_evaluator_rules.py @@ -21,11 +21,13 @@ def test_agent_source_allows_documented_env_methods(tmp_path): agent_file = _write_agent( tmp_path, """ +from GameLogic import Action, N_ACTIONS from RLInterfaces import BaseAgent class Agent(BaseAgent): def get_action(self, obs): mask = self.env.action_masks() + assert N_ACTIONS > int(Action.WAIT) return int(mask.argmax()) def train(self, total_timesteps, **kwargs): diff --git a/models/test/ppo_thuai9_best.zip b/models/test/ppo_thuai9_best.zip new file mode 100644 index 0000000000000000000000000000000000000000..36e630f13125ae52707770632766bea35ae3a9df GIT binary patch literal 277955 zcmeFa2Ut@}&@fC#Q4v(^C5oaVBE99zDqTRjbQJ@H8VDhwDk_3tZzwkGyfL!r<%iF&H`=94M8p7F~otd4Totd2-AN{=4HCm`B9<2>~23;d+FUR## zQBiy}xV9L9OfXD`$30zA#iE4h?8s=5Kq~Fxz^iFh_BhD0Q-uy(TNg&z%j+(PiZ*q0 za`qO=1l>s&6D5KgP^mC2K_E)V5ymJ7#5slp2gEtnKF;2r{e2R=&FvGTEUe9%Zz3y~<=I?&ra$UHvULKYvEoF(!P@iB|C z^mO6Inh8RD{FQK)$w5ih{^>vS5gC~*j!72@BO?`TOQS3!qs1w*w1gR+qPbnK-&Fo{vX*@x+Oq|v`IxRt#5X}>a;>6r< z@ySwNk9dKU7bTX(^J>J&6QsoOG6bS@Aulye7?Th!OAx2@G_AoJBo)T+qOy687S1L- zKR;h3uqc^VLIk(sJMp5DL;#5>n!LX#$y$_oJQHBU+p;;&>JEI5A3Pf)rWLdZbc> zvPeN%bi4y^AjiHmRT!NRn-Hz!l_!qnDdzMid2nMg#c4^3c`8I)J4*%AwUdlZao{Pq zu6gCfrl%;UP!C+3CW{wGCZr_D9C#t|LXJ=xPbTK2ONBgcl~Tkq?u&zn6Q>A7JO!9i zIhBmn!xJkM$kNk<(nw*JEREwqoK~|D9C&WEJ-oV}S_1X?{tbepOhHfk11J5ItH!v+-Xz~|m zcnMOTaKG$P z9y;_V%=K2e4oBpVwQ-Dz;dDZ&9ZA0~(@zwHS<#|&H)ia3Zh2h;>A2NY}S3 z3Kgs|EX)sm=6TneB2L%EY0gTMQe$sSBbAyR8OamKcxG8p#o5u-te)ZJ zj1s4AF-jx-FEH1F{~581>o16F)xJ*a{w;-H$Nh$sn_c}mM8_+za#1VJCgq~BELavZu1LT zg&Y*Nlyep_2~t^PY??SZQp6ec6xqKf_BS|w!-9p`FD%qLynjuu7XEjr9qXh14`Tjn z#Fkd|5epTrniEp3=>E0Z{W|V9bFlnnUnzvESQ#!RNmne>zZKK3<9X&_0JO4;! z9P+Q{RDaZO$XNfndm;r<+>%?BBnR3S8vbK}k z#ZoahZO-6Fio|jCNpriL+wWYo$Tk0M5YJ63HQO{LMv#^!$o}UrJ|oVxE{u1zur`kh zb+R5H;zD^#Ptky6?mZ+pTM*(Kl;|jn4Nvjp!g^2h@Z@k&bZX#0k#j;wpkv*5L2^J+ zpPc9%Z$Iv65a-e>O_D6`^W%{4<2xZN&co0D$D5<0WuMS=KdGhnKo=|D7&mdR5HlyY zjGVZ14_muGfd1(>Z%gkS7b}lm33eR)Ebj~jz5&+hVa`^6qW>!@1-ycO&H$@lyZ+UC za%xg|hMj_c8*hu?Xge!T1b%@5&OQTeZ4*4bd%5~qq}9yV#V5x#vsaXzeX5_>*~Q$& zGQ-~`te2I&Z>C>ByiHJI4Sgm5$s)Hz7b`1Suw5@Ft0WIUk$a+hqE#Pn4*p*;^)G

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System.Reflection.AssemblyInformationalVersionAttribute("1.0.0+9f7db73aae63536d35e02c009a73a2d19a4a6e8d")] +[assembly: System.Reflection.AssemblyInformationalVersionAttribute("1.0.0+6c55dad574f4c42903af5b9996d72a8b23b25485")] [assembly: System.Reflection.AssemblyProductAttribute("playback")] [assembly: System.Reflection.AssemblyTitleAttribute("playback")] [assembly: System.Reflection.AssemblyVersionAttribute("1.0.0.0")] diff --git a/playback/playback/obj/Debug/net8.0/playback.AssemblyInfoInputs.cache b/playback/playback/obj/Debug/net8.0/playback.AssemblyInfoInputs.cache index 6a8bee78..0b43f246 100644 --- a/playback/playback/obj/Debug/net8.0/playback.AssemblyInfoInputs.cache +++ b/playback/playback/obj/Debug/net8.0/playback.AssemblyInfoInputs.cache @@ -1 +1 @@ -31b111defe087f5513a86ad2324ba79a87d15b774f0b496a1f274a44a14013cf +90ce4265b1b829b754cf1927d47385c4c959368f721e3693edb0e14039a808ba From 29e8b76c0b2c67542fa06f64b9398add8d4a1827 Mon Sep 17 00:00:00 2001 From: jxd136 Date: Tue, 2 Jun 2026 09:55:03 +0800 Subject: [PATCH 2/2] chore: del --- logic/pve/submission/agent.py | 931 ---------------------------------- logic/pve/submission/model.pt | Bin 1249 -> 0 bytes 2 files changed, 931 deletions(-) delete mode 100644 logic/pve/submission/agent.py delete mode 100644 logic/pve/submission/model.pt diff --git a/logic/pve/submission/agent.py b/logic/pve/submission/agent.py deleted file mode 100644 index 83cacbd7..00000000 --- a/logic/pve/submission/agent.py +++ /dev/null @@ -1,931 +0,0 @@ -""" -THUAI9 PvE-RL Agent — 纯套利策略（v7）。 - -核心策略： -1. 开局抢占算力中心 → 累积 compute -2. 获取 TECH_1 + TECH_5（消除移动冷却）+ TECH_3（容量+50%） -3. 跨市场套利为主要（唯一）利润来源 -4. 不采集不生产——在 volatility=2.0 下套利效率远超生产 -5. A* 路径缓存 + 买入来源追踪 -""" -from __future__ import annotations - -import os -import math -import random -import heapq -from collections import deque -from typing import Any, Dict, List, Optional, Tuple - -import numpy as np -import torch -import torch.nn as nn - -from GameLogic import N_ACTIONS -from RLInterfaces import BaseAgent - - -class PolicyNet(nn.Module): - def __init__(self, obs_dim=82, act_dim=28, hidden=512): - super().__init__() - self.net = nn.Sequential( - nn.Linear(obs_dim, hidden), nn.BatchNorm1d(hidden), nn.ReLU(inplace=True), - nn.Dropout(0.15), - nn.Linear(hidden, hidden), nn.BatchNorm1d(hidden), nn.ReLU(inplace=True), - nn.Dropout(0.15), - nn.Linear(hidden, hidden), nn.BatchNorm1d(hidden), nn.ReLU(inplace=True), - nn.Dropout(0.15), - nn.Linear(hidden, act_dim), - ) - def forward(self, obs, mask): - logits = self.net(obs) - logits[~mask] = -1e9 - return logits - - -# ── 动作常量 ────────────────────────────────────────────────────────────────── -WAIT, MOVE_UP, MOVE_DOWN, MOVE_LEFT, MOVE_RIGHT = 0, 1, 2, 3, 4 -BUY, HARVEST, DEPOSIT, LOAD, OCCUPY = 5, 11, 12, 18, 19 -SELL_0, SELL_1, SELL_2, SELL_3, SELL_4 = 6, 7, 8, 9, 10 -PRODUCE_0, PRODUCE_1, PRODUCE_2, PRODUCE_3, PRODUCE_4 = 13, 14, 15, 16, 17 -TECH_0, TECH_1, TECH_2, TECH_3, TECH_4, TECH_5, TECH_6, TECH_7 = range(20, 28) - -SELL_ACTIONS = [SELL_0, SELL_1, SELL_2, SELL_3, SELL_4] -PROD_ACTIONS = [PRODUCE_0, PRODUCE_1, PRODUCE_2, PRODUCE_3, PRODUCE_4] -TECH_ACTIONS = list(range(20, 28)) - -MOVE_DELTA = {MOVE_UP: (-1, 0), MOVE_DOWN: (1, 0), MOVE_LEFT: (0, -1), MOVE_RIGHT: (0, 1)} -MOVE_SET = {MOVE_UP, MOVE_DOWN, MOVE_LEFT, MOVE_RIGHT} - -# 科技名 → TECH_x 映射（用于按需购买） -TECH_KEY_TO_ID = { - "efficiency": TECH_1, - "path_optimization": TECH_5, - "durability": TECH_3, - "marketing": TECH_2, - "compute_expansion": TECH_7, -} - -# 科技购买优先级：路径优化→扩容→提价（path_opt 消除移动冷却最优先） -TECH_PRIORITY = ["efficiency", "path_optimization", "durability", "marketing"] - -PRODUCT = { - 0: {"cost": 10, "raw": 5, "lo": 40, "hi": 120, "time": 5.0}, - 1: {"cost": 5, "raw": 3, "lo": 20, "hi": 60, "time": 4.0}, - 2: {"cost": 1, "raw": 1, "lo": 4, "hi": 12, "time": 2.0}, - 3: {"cost": 8, "raw": 4, "lo": 32, "hi": 96, "time": 6.0}, - 4: {"cost": 3, "raw": 2, "lo": 12, "hi": 24, "time": 1.0}, -} - -# 观测索引 -OFF_X, OFF_Y, OFF_RAW, OFF_PROD = 0, 1, 3, 4 -OFF_BUSY, OFF_MONEY, OFF_COMP, OFF_TIME = 9, 10, 11, 12 -OFF_RES, OFF_CENTER, OFF_MARKET, OFF_TECH = 22, 34, 46, 74 - -CAP = 30.0 - - -def _price(pid, norm): - p = PRODUCT[pid] - rng = max(1.0, p["hi"] - p["lo"]) - return norm * rng + p["lo"] - - -class Agent(BaseAgent): - def __init__(self, env, mode="rule"): - super().__init__(env) - self.mode = mode - self.policy_net = None - - self.x = self.y = 0 - self.H = self.W = 0 - self._map_ok = False - - # 实体位置（obs 每步刷新） - self.mkts = {} # mid → (x, y) - self.res = {} # rid → (x, y) - self.ctrs = {} # cid → (x, y) - - # 经济 - self.money = 0.0 - self.compute = 0.0 - - # 记忆 - self._techs = set() - self._obs = set() # 已知障碍 - self._black = set() # 黑名单（不可达目标） - self._black_t = {} - self._cells = set() # 已访问 - self._fail = {} - - # 目标系统 - self._goal = None # (gx, gy) - self._goal_act = None # 到达后执行的动作 - self._goal_steps = 0 - self._goal_start_dist = 0 - self._state = "EXPLORE" - - # 卡住检测 - self._pos_hist = [] - - # Episode 检测 - self._last_t = -1.0 - self._eps_steps = 0 - - # 重规划 - self._replan = True - - # ── 新增：路径缓存 ── - self._path_cache = {} # (sx,sy,gx,gy) → [actions] or None - self._path_hits = 0 - self._path_misses = 0 - - # ── 新增：买入追踪 ── - self._last_buy_mid = None # 最近买入的市场 id - self._has_bought = False # 是否持有通过 BUY 获得的产品 - - # ── 新增：算力中心追踪 ── - self._occupied = set() # 已占领的算力中心 id - - # ── 新增：科技购买追踪（防止反复尝试）── - self._tech_attempted = {} # tech_key → fail_count - - # ═══════════════════════════════════════════════════════════════════════════ - # 观测 - # ═══════════════════════════════════════════════════════════════════════════ - - def _reset(self): - self._obs.clear() - self._black.clear() - self._black_t.clear() - self._cells.clear() - self._fail.clear() - self._pos_hist.clear() - self._techs.clear() - self._goal = None - self._goal_act = None - self._goal_steps = 0 - self._goal_start_dist = 0 - self._state = "EXPLORE" - self._replan = True - self._eps_steps = 0 - self._path_cache.clear() - self._last_buy_mid = None - self._has_bought = False - self._occupied.clear() - self._tech_attempted.clear() - - def _read_obs(self, obs): - # 地图检测 - if not self._map_ok: - m = 10 ** (obs[OFF_MONEY] * 5) - 1 - if m > 150: - self.H = self.W = 5 - elif m > 40: - self.H = self.W = 10 - else: - self.H = self.W = 15 - self._map_ok = True - - # Episode 重置检测 - t = obs[OFF_TIME] - if self._last_t >= 0 and t < self._last_t - 0.05: - self._reset() - self._last_t = t - self._eps_steps += 1 - - H, W = max(1, self.H), max(1, self.W) - - # 位置 - self.x = int(round(obs[OFF_X] * H)) - self.y = int(round(obs[OFF_Y] * W)) - - # 经济 - self.compute = obs[OFF_COMP] * 100.0 - self.money = 10 ** (obs[OFF_MONEY] * 5) - 1 - - # 资源 - self.res.clear() - for i in range(4): - b = OFF_RES + i * 3 - dx, dy = obs[b], obs[b + 1] - if abs(dx) + abs(dy) < 1e-6: - continue - rx = self.x + int(round(dx * H)) - ry = self.y + int(round(dy * W)) - if 0 <= rx < H and 0 <= ry < W: - self.res[i] = (rx, ry) - - # 中心 - self.ctrs.clear() - for i in range(3): - b = OFF_CENTER + i * 4 - dx, dy = obs[b], obs[b + 1] - if abs(dx) + abs(dy) < 1e-6: - continue - cx = self.x + int(round(dx * H)) - cy = self.y + int(round(dy * W)) - if 0 <= cx < H and 0 <= cy < W: - self.ctrs[i] = (cx, cy) - - # 市场 - self.mkts.clear() - for i in range(4): - b = OFF_MARKET + i * 7 - dx, dy = obs[b], obs[b + 1] - if abs(dx) + abs(dy) < 1e-6: - # 站在市场上：位置就是自身 - mx, my = self.x, self.y - else: - mx = self.x + int(round(dx * H)) - my = self.y + int(round(dy * W)) - if 0 <= mx < H and 0 <= my < W: - self.mkts[i] = (mx, my) - - # 检测新占领的算力中心 - self._check_new_centers(obs) - - # 更新买入追踪：如果身上没有产品了，清除标记 - if not self._has_prod(obs): - self._has_bought = False - self._last_buy_mid = None - - def _check_new_centers(self, obs): - """检测是否有新占领的算力中心。""" - for i in range(3): - b = OFF_CENTER + i * 4 - if b + 2 < len(obs) and obs[b + 2] > 0.5: - self._occupied.add(i) - - # ═══════════════════════════════════════════════════════════════════════════ - # 辅助 - # ═══════════════════════════════════════════════════════════════════════════ - - def _prices(self, obs, mid): - b = OFF_MARKET + mid * 7 + 2 - return {pid: _price(pid, obs[b + pid]) for pid in range(5)} - - def _all_prices(self, obs): - return {mid: self._prices(obs, mid) for mid in self.mkts} - - def _techs_owned(self, obs): - return [obs[OFF_TECH + i] > 0.5 for i in range(8)] - - def _busy(self, obs): - return obs[OFF_BUSY] * 10.0 > 0.5 - - def _has_raw(self, obs): - return obs[OFF_RAW] * CAP > 0.5 - - def _has_prod(self, obs): - return any(obs[OFF_PROD + i] * CAP > 0.01 for i in range(5)) - - def _raw_qty(self, obs): - return obs[OFF_RAW] * CAP - - def _prod_inv(self, obs): - return {pid: obs[OFF_PROD + pid] * CAP for pid in range(5) - if obs[OFF_PROD + pid] * CAP > 0.01} - - def _at_factory(self): - return self.x == 0 and self.y == 0 - - def _capacity(self, obs): - return 45.0 if self._techs_owned(obs)[3] else 30.0 - - # ═══════════════════════════════════════════════════════════════════════════ - # 障碍物学习 - # ═══════════════════════════════════════════════════════════════════════════ - - def _learn_obs(self, mask): - """标记被封堵的移动方向。""" - for act, (dx, dy) in MOVE_DELTA.items(): - if not mask[act]: - nx, ny = self.x + dx, self.y + dy - if 0 <= nx < self.H and 0 <= ny < self.W: - if (nx, ny) not in self._cells: - self._obs.add((nx, ny)) - - def _check_stuck(self): - self._pos_hist.append((self.x, self.y)) - if len(self._pos_hist) > 30: - self._pos_hist.pop(0) - if len(self._pos_hist) >= 25: - if len(set(self._pos_hist)) <= 3: - self._obs.clear() - self._black.clear() - self._black_t.clear() - self._pos_hist.clear() - self._path_cache.clear() - self._goal = None - self._goal_act = None - self._replan = True - - # ═══════════════════════════════════════════════════════════════════════════ - # A*（带缓存） - # ═══════════════════════════════════════════════════════════════════════════ - - def _astar(self, gx, gy): - if (self.x, self.y) == (gx, gy): - return [] - if not (0 <= gx < self.H and 0 <= gy < self.W): - return None - if (gx, gy) in self._obs: - return None - - # 检查缓存 - cache_key = (self.x, self.y, gx, gy) - if cache_key in self._path_cache: - self._path_hits += 1 - return self._path_cache[cache_key] - - self._path_misses += 1 - - start = (self.x, self.y) - goal = (gx, gy) - open_set = [(abs(start[0] - gx) + abs(start[1] - gy), 0, start, None, -1)] - came_from = {} - cost = {start: 0} - found = None - - while open_set: - _, c, pos, prev, act = heapq.heappop(open_set) - if pos in came_from: - continue - came_from[pos] = (prev, act) - if pos == goal: - found = pos - break - for a, (dx, dy) in MOVE_DELTA.items(): - nx, ny = pos[0] + dx, pos[1] + dy - if not (0 <= nx < self.H and 0 <= ny < self.W): - continue - if (nx, ny) in self._obs: - continue - nc = c + 1 - if (nx, ny) not in cost or nc < cost[(nx, ny)]: - cost[(nx, ny)] = nc - heapq.heappush(open_set, (nc + abs(nx - gx) + abs(ny - gy), nc, (nx, ny), pos, a)) - - if found is None: - self._path_cache[cache_key] = None - return None - - acts = [] - cur = found - while cur != start: - prev, a = came_from[cur] - acts.append(a) - cur = prev - acts.reverse() - - # 缓存路径（限制缓存大小） - if len(self._path_cache) < 5000: - self._path_cache[cache_key] = acts - - return acts - - # ═══════════════════════════════════════════════════════════════════════════ - # 目标超时检测 - # ═══════════════════════════════════════════════════════════════════════════ - - def _goal_timed_out(self): - if self._goal is None: - return False - gx, gy = self._goal - dist = abs(self.x - gx) + abs(self.y - gy) - - if self._goal_steps == 0: - self._goal_start_dist = dist - - self._goal_steps += 1 - timeout = 120 + self._goal_start_dist * 5 - - if self._goal_steps > timeout: - return True - if self._goal_steps > 40 and dist >= self._goal_start_dist: - return True - return False - - # ═══════════════════════════════════════════════════════════════════════════ - # 黑名单维护 - # ═══════════════════════════════════════════════════════════════════════════ - - def _blacklist_maintain(self): - expired = [p for p, t in self._black_t.items() if self._eps_steps - t > 800] - for p in expired: - self._black.discard(p) - del self._black_t[p] - while len(self._black) > 12: - old = min((p for p in self._black if p != (0, 0)), - key=lambda p: self._black_t.get(p, 0), default=None) - if old is None: - break - self._black.discard(old) - del self._black_t[old] - - def _blacklist(self, pos): - if pos and pos != (0, 0): - self._black.add(pos) - self._black_t[pos] = self._eps_steps - - # ═══════════════════════════════════════════════════════════════════════════ - # 导航 - # ═══════════════════════════════════════════════════════════════════════════ - - def _move_to(self, mask): - """向 _goal 移动一步。""" - if self._goal is None: - return None - gx, gy = self._goal - - # 已到达 - if (self.x, self.y) == (gx, gy): - return None - - # 超时检测 - if self._goal_timed_out(): - self._blacklist((gx, gy)) - self._goal = None - self._goal_act = None - return None - - # A* - path = self._astar(gx, gy) - if path and len(path) > 0 and mask[path[0]]: - return path[0] - - # 贪心兜底 - best, best_d = None, 999 - for a, (dx, dy) in MOVE_DELTA.items(): - if mask[a]: - nx, ny = self.x + dx, self.y + dy - if 0 <= nx < self.H and 0 <= ny < self.W: - d = abs(nx - gx) + abs(ny - gy) - if d < best_d: - best_d = d - best = a - - if best is None: - fc = self._fail.get((gx, gy), 0) + 1 - self._fail[(gx, gy)] = fc - if fc >= 3: - self._blacklist((gx, gy)) - self._fail.pop((gx, gy), None) - self._goal = None - self._goal_act = None - return best - - def _set_goal(self, gx, gy, action=None): - """设置新目标并重置计时器。""" - self._goal = (gx, gy) if gx is not None else None - self._goal_act = action - self._goal_steps = 0 - if gx is not None: - self._goal_start_dist = abs(self.x - gx) + abs(self.y - gy) - self._replan = False - - # ═══════════════════════════════════════════════════════════════════════════ - # 探索（BFS 最近未访问格） - # ═══════════════════════════════════════════════════════════════════════════ - - def _explore(self): - self._cells.add((self.x, self.y)) - q = deque([(self.x, self.y)]) - vis = {(self.x, self.y)} - while q: - cx, cy = q.popleft() - if (cx, cy) not in self._cells and (cx, cy) not in self._obs: - return (cx, cy) - for dx, dy in [(0, 1), (0, -1), (1, 0), (-1, 0)]: - nx, ny = cx + dx, cy + dy - if 0 <= nx < self.H and 0 <= ny < self.W and (nx, ny) not in vis: - vis.add((nx, ny)) - q.append((nx, ny)) - return None - - # ═══════════════════════════════════════════════════════════════════════════ - # 套利系统（核心） - # ═══════════════════════════════════════════════════════════════════════════ - - def _best_arbitrage(self, obs): - """找最佳套利机会。(buy_mid, sell_mid, pid, margin, profit_est) 或 None。""" - if len(self.mkts) < 2: - return None - prices = self._all_prices(obs) - - best = None - best_score = float("-inf") - - for bmid, bp in prices.items(): - bx, by = self.mkts[bmid] - if (bx, by) in self._black: - continue - for smid, sp in prices.items(): - if bmid == smid: - continue - sx, sy = self.mkts[smid] - if (sx, sy) in self._black: - continue - for pid in range(5): - buy_p = bp.get(pid, 0) - sell_p = sp.get(pid, 0) - if buy_p <= 0 or sell_p <= buy_p * 1.02: - continue - margin = sell_p - buy_p - # 综合评分：利润率 - 距离惩罚 - profit_rate = margin / max(buy_p, 1) - travel = abs(self.x - bx) + abs(self.y - by) + abs(bx - sx) + abs(by - sy) - # 有效的单步利润率（travel cost per unit is amortized over capacity） - cap = self._capacity(obs) - # 能买多少单位 - affordable = int(self.money / max(buy_p, 1)) if self.money > 0 else 0 - units = min(affordable, int(cap)) - if units <= 0: - continue - total_profit = units * margin - # 评分：单步期望收益（buy: units*2 tick, sell: 一次卖全部仅2 tick） - steps_est = travel + units * 2 + 2 - if steps_est <= 0: - continue - score = total_profit / steps_est - if score > best_score: - best_score = score - best = (bmid, smid, pid, margin, total_profit, buy_p, sell_p, units) - - return best - - def _best_sell_market(self, obs, avoid_mid=None): - """为身上产品找到最佳卖出市场。""" - prod_inv = self._prod_inv(obs) - if not prod_inv: - return None - - prices = self._all_prices(obs) - best = None - best_total = 0 - - for mid, mp in prices.items(): - if mid == avoid_mid: - continue - mx, my = self.mkts[mid] - if (mx, my) in self._black: - continue - total = 0 - sellable = 0 - for pid, qty in prod_inv.items(): - price = mp.get(pid, 0) - if price > 0: - total += qty * price - sellable += qty - if sellable > 0 and total > best_total: - best_total = total - d = abs(self.x - mx) + abs(self.y - my) - best = (mid, total, sellable, d) - - return best - - # ═══════════════════════════════════════════════════════════════════════════ - # 算力中心与科技 - # ═══════════════════════════════════════════════════════════════════════════ - - def _nearest_closed_center(self): - """返回最近的未占领算力中心。""" - best_cid = None - best_d = 999 - for cid, (cx, cy) in self.ctrs.items(): - if cid in self._occupied: - continue - if (cx, cy) in self._black: - continue - path = self._astar(cx, cy) - if path is None: - self._blacklist((cx, cy)) - continue - d = abs(self.x - cx) + abs(self.y - cy) - if d < best_d: - best_d = d - best_cid = cid - return best_cid - - def _next_tech_to_buy(self, obs): - """返回下一个应该购买的科技 TECH_x ID（mask 已检查），或 None。""" - techs = self._techs_owned(obs) - for key in TECH_PRIORITY: - tid = TECH_KEY_TO_ID[key] - idx = tid - TECH_0 - if techs[idx]: - continue # 已拥有 - # 检查失败次数（避免死循环） - fails = self._tech_attempted.get(key, 0) - if fails >= 5: - continue - return tid - return None - - def _should_get_techs(self, obs, mask): - """判断是否应该去工厂买科技。""" - # 已经有 path_optimization → 不需要 - techs = self._techs_owned(obs) - if techs[5] and techs[3]: - return False - if self._tech_attempted.get("path_optimization", 0) >= 5: - return False - # 在工厂且 mask 允许科技购买 - if not self._at_factory(): - return False - tid = self._next_tech_to_buy(obs) - if tid is None or not mask[tid]: - return False - return True - - # ═══════════════════════════════════════════════════════════════════════════ - # 主策略 - # ═══════════════════════════════════════════════════════════════════════════ - - def _plan(self, obs, mask): - self._blacklist_maintain() - - techs = self._techs_owned(obs) - has_prod = self._has_prod(obs) - prod_inv = self._prod_inv(obs) - prod_qty = sum(prod_inv.values()) - has_path_opt = techs[5] - cap = self._capacity(obs) - - # 站哪个市场旁？ - at_mid = self._which_market_adjacent() - - # ═══════════════════════════════════════════════════════════════════════ - # P0: 在买入市场 → 继续批量买入（直到满了或没钱了再走） - # ═══════════════════════════════════════════════════════════════════════ - if has_prod and at_mid is not None and at_mid == self._last_buy_mid: - # 在买入市场，检查能否继续买 - if mask[BUY] and prod_qty < cap and self.money >= 1.0: - self._state = "ARB_BUY" - self._set_goal(None, None, BUY) - return - - # ═══════════════════════════════════════════════════════════════════════ - # P1: 在非买入市场旁 → 卖出手持产品 - # ═══════════════════════════════════════════════════════════════════════ - if has_prod and at_mid is not None and at_mid != self._last_buy_mid: - for pid in list(prod_inv.keys()): - if mask[SELL_0 + pid]: - self._state = "SELL" - self._set_goal(None, None, SELL_0 + pid) - return - best_sell = self._best_sell_market(obs, avoid_mid=self._last_buy_mid) - if best_sell: - self._state = "GO_SELL" - self._set_goal(*self.mkts[best_sell[0]], None) - return - - # ═══════════════════════════════════════════════════════════════════════ - # P1.5: 在工厂 → 买科技或等待算力 - # ═══════════════════════════════════════════════════════════════════════ - if self._at_factory(): - tid = self._next_tech_to_buy(obs) - if tid is not None and mask[tid]: - self._state = "TECH" - self._set_goal(None, None, tid) - return - # 还需要科技但算力不够→在工厂等待算力积累（避免第二趟往返） - if tid is not None and self._occupied and not has_prod: - self._state = "WAIT_COMPUTE" - self._goal = None - self._goal_act = None - return - - # ═══════════════════════════════════════════════════════════════════════ - # P2: 需要核心科技且有足够算力 → 回工厂（仅一次） - # ═══════════════════════════════════════════════════════════════════════ - need_tech = (not techs[1]) or (not techs[5]) or (not techs[3]) - if need_tech and self.compute >= 90 and not self._at_factory(): - path = self._astar(0, 0) - if path is not None and len(path) <= 20: - self._state = "GO_FACTORY" - self._set_goal(0, 0, None) - return - - # ═══════════════════════════════════════════════════════════════════════ - # P3: 有产品但不在市场 → 去最佳卖出市场 - # ═══════════════════════════════════════════════════════════════════════ - if has_prod and at_mid is None: - avoid = self._last_buy_mid if self._has_bought else None - best_sell = self._best_sell_market(obs, avoid_mid=avoid) - if best_sell: - self._state = "GO_SELL" - self._set_goal(*self.mkts[best_sell[0]], None) - return - - # ═══════════════════════════════════════════════════════════════════════ - # P3: 在市场旁且空手 → 开始批量买入 - # ═══════════════════════════════════════════════════════════════════════ - if not has_prod and at_mid is not None and mask[BUY] and self.money >= 1.0: - self._state = "ARB_BUY" - self._last_buy_mid = at_mid - self._has_bought = True - self._set_goal(None, None, BUY) - return - - # ═══════════════════════════════════════════════════════════════════════ - # P4: 未占领任何中心 → 主动去最近的计算中心（开局最关键！） - # ═══════════════════════════════════════════════════════════════════════ - if not self._occupied and self.ctrs and not has_prod: - cid = self._nearest_closed_center() - if cid is not None: - path = self._astar(*self.ctrs[cid]) - if path is not None: - self._state = "GO_CTR" - self._set_goal(*self.ctrs[cid], None) - return - - # ═══════════════════════════════════════════════════════════════════════ - # P6: 空手 → 找最佳套利对 → 去买入市场 - # ═══════════════════════════════════════════════════════════════════════ - if not has_prod and len(self.mkts) >= 2: - arb = self._best_arbitrage(obs) - if arb and arb[4] > 2: # 降低门槛，微小利润也能积累 - bmid = arb[0] - bx, by = self.mkts[bmid] - self._state = "ARB_GO_BUY" - self._set_goal(bx, by, None) - return - - # ═══════════════════════════════════════════════════════════════════════ - # P7: 有产品但没找到卖点 → 去最近非买入市场（兜底） - # ═══════════════════════════════════════════════════════════════════════ - if has_prod and self.mkts: - best_mid = None - best_d = 999 - for mid, (mx, my) in self.mkts.items(): - if mid == self._last_buy_mid: - continue - if (mx, my) in self._black: - continue - d = abs(self.x - mx) + abs(self.y - my) - if d < best_d: - best_d = d - best_mid = mid - if best_mid is not None: - self._state = "GO_SELL" - self._set_goal(*self.mkts[best_mid], None) - return - - # ═══════════════════════════════════════════════════════════════════════ - # P8: 探索/去最近市场 - # ═══════════════════════════════════════════════════════════════════════ - if self.mkts: - best_mid = None - best_score = -999 - for mid, (mx, my) in self.mkts.items(): - if (mx, my) in self._black: - continue - d = max(1, abs(self.x - mx) + abs(self.y - my)) - score = 50.0 / d - if score > best_score: - best_score = score - best_mid = mid - if best_mid is not None: - self._state = "GO_MKT" - self._set_goal(*self.mkts[best_mid], None) - return - - # ═══════════════════════════════════════════════════════════════════════ - # P11: 探索未知区域 - # ═══════════════════════════════════════════════════════════════════════ - ep = self._explore() - if ep: - self._state = "EXPLORE" - self._set_goal(*ep, None) - return - - self._state = "IDLE" - self._goal = None - - def _which_market_adjacent(self): - """返回当前相邻的市场 id。""" - for mid, (mx, my) in self.mkts.items(): - if abs(self.x - mx) + abs(self.y - my) <= 1: - return mid - return None - - # ═══════════════════════════════════════════════════════════════════════════ - # 主入口 - # ═══════════════════════════════════════════════════════════════════════════ - - def get_action(self, observation): - mask = self.env.action_masks() - - if self.mode == "rl" and self.policy_net is not None: - self._read_obs(observation) - self._check_stuck() - if self._busy(observation): - return WAIT - device = next(self.policy_net.parameters()).device - ot = torch.tensor(observation, dtype=torch.float32, device=device).unsqueeze(0) - mt = torch.tensor(mask, dtype=torch.bool, device=device).unsqueeze(0) - with torch.no_grad(): - return int(self.policy_net(ot, mt).argmax(dim=1).item()) - - # 规则模式 - valid = np.where(mask)[0] - self._read_obs(observation) - self._check_stuck() - - if self._busy(observation): - return WAIT - - # 只在非 busy 时学习障碍物 - self._learn_obs(mask) - - # ══ OCCUPY：最多占2个中心加速算力 ══ - if mask[OCCUPY] and len(self._occupied) < 2: - self._state = "OCCUPY" - self._set_goal(None, None, OCCUPY) - self._replan = False - self._goal_act = OCCUPY - - # 需要 replan？ - if self._goal_act is not None: - if not mask[self._goal_act]: - # 动作不再可用 - key = None - for k, v in TECH_KEY_TO_ID.items(): - if v == self._goal_act: - key = k - break - if key: - self._tech_attempted[key] = self._tech_attempted.get(key, 0) + 1 - self._plan(observation, mask) - elif self._replan or self._goal is None: - self._plan(observation, mask) - elif self._goal is not None and (self.x, self.y) == self._goal: - self._plan(observation, mask) - - # 执行动作 - if self._goal_act is not None and mask[self._goal_act]: - a = self._goal_act - self._goal_act = None - self._replan = True - elif self._goal is not None: - a = self._move_to(mask) - if a is None: - if (self.x, self.y) == self._goal: - # 到达目标但没本地动作 → replan - pass - self._goal = None - self._replan = True - a = (list(set(valid) & MOVE_SET) or [WAIT])[0] - else: - # 无目标，尝试移动或等待 - a = (list(set(valid) & MOVE_SET) or [WAIT])[0] - - if a is None: - a = WAIT - self._cells.add((self.x, self.y)) - return a - - # ═══════════════════════════════════════════════════════════════════════════ - # 训练 + 持久化 - # ═══════════════════════════════════════════════════════════════════════════ - - def train(self, total_timesteps=50000, save_dir="submission/", eval_freq=4096, **kwargs): - os.makedirs(save_dir, exist_ok=True) - scores = [] - best = -float("inf") - obs = self.reset() - for t in range(total_timesteps): - a = self.get_action(obs) - obs, r, term, trunc, info = self.step(a) - if term or trunc: - scores.append(info.get("score", 0)) - obs = self.reset() - if (t + 1) % eval_freq == 0 and len(scores) >= 3: - m = np.mean(scores[-3:]) - if m > best: - best = m - self.save(os.path.join(save_dir, "model_best.pt")) - self.save(os.path.join(save_dir, "model_final.pt")) - return {"total_timesteps": total_timesteps, "episodes": len(scores), - "mean_score_last10": np.mean(scores[-10:]) if scores else 0, - "best_mean_score": best} - - def save(self, path): - d = {"mode": self.mode} - if self.policy_net is not None: - d["policy_net"] = self.policy_net.state_dict() - torch.save(d, path) - - @classmethod - def load(cls, path, env): - d = torch.load(path, map_location="cpu", weights_only=False) - a = cls(env, mode=d.get("mode", "rule")) - if "policy_net" in d: - a.policy_net = PolicyNet() - a.policy_net.load_state_dict(d["policy_net"]) - a.policy_net.eval() - a.policy_net.to("cuda" if torch.cuda.is_available() else "cpu") - a.mode = "rl" - return a diff --git a/logic/pve/submission/model.pt b/logic/pve/submission/model.pt deleted file mode 100644 index 7b1fa412a438154e6c868792244e701e5b9a7de4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1249 zcmWIWW@cev;NW1u0DKH047vF!sX6*7i6x181=%@nP67;3XrO_qwvZtLXarboAtRVs zRGO1o$W*Ks;LXmVGTHOJEKnMR18`a<%Af$XOfN0JC^xYrzAUwCU=xWJeTWcS5+>7j)EFT-F0ivUoP(l1Ue$^^zhRbmM+qT@4D+?)(Zc8Y)h7diQfqUrHj#Fj6hLDac8HAH|d) zVAg;*1fKNJH6zC%FN)@CaLv#(gRT`hU}aFW1_1*Z8UR?MJiwccO$Vw&j#(G37nF!W n07kcivNjO>07n`q`q