Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess

This paper presents a new approach that integrates deep learning with computational chess, using both the Mixture of Experts (MoE) method and Monte-Carlo Tree Search (MCTS). Our methodology employs a suite of specialized models, each designed to respond to specific changes in the game's input data. This results in a framework with sparsely activated models, which provides significant computational benefits. Our framework combines the MoE method with MCTS, in order to align it with the strategic phases of chess, thus …

chess computational cs.lg deep learning experts game methodology mixture of experts moe monte-carlo paper search tree