Mastering Chess with a Transformer Model

Chess has long been regarded as a proving ground for advancing artificial intelligence (AI). The importance of strategic planning and rational decisionmaking in strong chess play make the game an ideal domain in which to test systems aiming to master these human-like cognitive capabilities. Traditional chess engines employ a specialized tree-search algorithm paired with a handcrafted evaluation function. Most modern chess engines follow this recipe but use an efficiently updated neural network (NNUE) rather than a handcrafted evaluation function. The AlphaZero engine [13] introduced a different recipe based on Monte Carlo Tree Search (MCTS) and deep neural networks.