PokaiTrainer：将信念状态搜索扩展至具备竞争力的宝可梦电子竞技游戏（VGC）
PokaiTrainer: Scaling Equilibrium Search to Competitive Pokémon VGC
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中文总结 AI 辅助
PokaiTrainer将Student of Games算法适配至自主开发的Rust战斗引擎PokaiEngine，构建出击败多数人类玩家、峰值进入宝可梦VGC天梯前500的竞技智能体。
中文摘要 AI 辅助
决策时间的均衡搜索已使扑克达到超人类水平，但迄今为止，该方法依赖于可处理的子博弈：每次决策仅有少量动作、随机性局限于发牌、每次仅一名玩家行动。官方双打格式的竞技宝可梦（VGC）同时打破了这三个假设：双方玩家从包含数百种选项的联合菜单中同时行动，每个联合动作会产生数百种随机结果，且对手的后备宝可梦与能力值分配均为隐藏信息。我们着手构建一个强大的VGC智能体，并报告为此所做的工作：我们的Rust战斗引擎PokaiEngine可一次性枚举联合动作的完整加权结果分布，准确率约为宝可梦Showdown的99%，且成本仅为采样方法的一小部分；在该引擎之上，PokaiTrainer将Student of Games算法适配至该规模，在显式计算预算下，将每个决策求解为关于公共信念状态与增长子博弈的贝叶斯矩阵博弈。在Showdown平台的三局两胜实时天梯中，该智能体对阵平均Elo约为1320的人类玩家，在150组对局中胜率达59%；它稳定在1350-1400 Elo区间，峰值时短暂进入该格式的前500名。
英文摘要
Decision-time equilibrium search carried poker to superhuman play, but it has so far relied on tractable subgames: a handful of actions per decision, chance confined to card deals, one player moving at a time. Competitive Pokémon in its official doubles format (VGC) breaks all three assumptions at once. Both players act simultaneously from joint menus in the hundreds, each joint action resolves to hundreds of stochastic outcomes, and the opponent's reserves and stat allocations are hidden. No prior Pokémon agent performs equilibrium search, and whether it scales to this regime was open; we show that it does, and report what it took. PokaiEngine, our Rust battle engine, enumerates a joint action's full weighted outcome distribution in one pass, at ${\sim}99\%$ parity with Pokémon Showdown and a fraction of the cost of sampling it. PokaiTrainer adapts Student of Games to this scale and trains it by self-play over hundreds of human teams. Each decision is solved by counterfactual regret minimization as a Bayesian matrix game over public belief states, subgames grow under an explicit compute budget, and value targets are harvested from the interior of every solve and grounded by realized outcomes. The strength is in the search. The network's policy alone loses even to a shallow heuristic search. PokaiTrainer is, to our knowledge, the first VGC agent rated on the live Showdown ladder. Under open team sheets it wins 59% of 150 best-of-three sets against a human field averaging ${\sim}1320$ Elo, holds a 1350-1400 Elo band, and at its peak reached 1492 Elo, entering the format's top 500.
