AI RESEARCH
MAPLE: Multi-State Aggregated Policy Evaluation for AlphaZero in Imperfect-Information Games
arXiv CS.AI
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ArXi:2605.24139v1 Announce Type: new Imperfect-information games (IIGs) are challenging, as players must make decisions without fully observing the true game state. While AlphaZero has achieved remarkable success in perfect-information games, extending it to IIGs remains difficult. Existing search-based approaches, such as Perfect Information Monte Carlo (PIMC), suffer from strategy fusion, while Information Set Monte Carlo Tree Search (IS-MCTS) incurs high computational cost when combined with neural networks.