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PokaiTrainer: Scaling Belief-State Search to Competitive Pok\'emon VGC
AI InsightThis paper extends decision-time equilibrium search from tractable subgames (few actions, public deals) to competitive Pokemon VGC, where both players act simultaneously from hundreds of actions, outcomes are stochastic, and information is hidden. It introduces PokaiEngine, a Rust battle engine that achieves ~99% parity with Pokemon Showdown, demonstrating that belief-state search can scale to such complex game settings.Key TakeawayGame search expands from sequential, public-deal games to simultaneous-action VGC with hidden information.Why It MattersBreaks three core assumptions of classic game search, showing belief-state search scales in complex real-world games and offering a new benchmark for AI research.Who's Affected- AI ResearchersGain a reproducible VGC game-search baseline, advancing research in simultaneous-move and hidden-information games.
- Game AI DevelopersCan borrow the efficient enumeration engine and search architecture for other complex strategy games.
- Game Theory ResearchersA real-world case validating decision-time equilibrium search beyond traditional assumptions.
What's NextWatch for extensions to multi-player and larger hidden-state games, and whether PokaiEngine is open-sourced.Importance 65/100