MAPLE: Multi-State Aggregated Policy Evaluation for AlphaZero in Imperfect-Information Games
Researchers have developed a new tree search method called MAPLE, designed to improve the performance of AlphaZero-style algorithms in imperfect-information games. Unlike previous methods that struggle with strategy fusion or high computational costs, MAPLE aggregates policy and value evaluations from multiple sampled world states within a single search tree. Experiments on Phantom Go and Dark Hex demonstrated significant Elo improvements, showing MAPLE's effectiveness for AlphaZero-like learning in complex games. AI
IMPACT Introduces a novel approach for AI agents to master complex games with incomplete information.