Enhanced application of deep neural networks in Einstein Chess research
As a perfect information game with added randomness, Einstein Chess poses challenges due to the uncertainty brought by the dice rolls, which increases the difficulty of strategy design and position evaluation.This paper proposes a modified deep learning approach in response to the rules of Einstein Chess.First, improvements and designs are made to the Alpha( go) Zero neural network model, enabling it to accurately evaluate various board states and generate effective game strategies.Then, by combining an enhanced residual neural network and Monte Carlo tree search, chessboard features are extracted and position evaluation is conducted to dynamically generate strategies and make decisions.Reinforcement learning is employed, using expected win rate as the criterion, to continually optimize the weights through self-play and improve the effectiveness of strategy generation.Our experimental results indicate the improved deep learning approach outperforms the algorithms used by the champion team in the National Computer Games Tournament, further validating the effectiveness and feasibility of deep learning methods in the context of Einstein Chess as a random perfect information game.
computer gamesimperfect information gamesEinstein Chessdeep neural network
国家科技期刊平台
NSTL
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