Design and Implementation of an Einstein Chess Intelligent Gaming System Based on Policy-Value Network

Einstein Chess is a board game characterized by randomness and special movement rules, presenting new challenges for artificial intelligence systems due to its complex game mechanics. This paper proposes an intelligent game system for Einstein Chess based on a policy-value network, effectively addressing randomness and special rules through an improved AlphaZero framework. The main innovations of the system include: (1) the design of a 16-channel state representation method that effectively encodes the board state and move history; (2) the construction of a deep convolutional network based on the PyTorch framework, utilizing shared feature extraction layers and a dual-head output structure; (3) the optimization of randomness response strategies through an improved Monte Carlo Tree Search algorithm. Experimental results indicate that the policy-value network-based intelligent game system for Einstein Chess achieved a significant advantage compared to the champion program of the 2024 Liaoning Province Computer Game Competition, attaining a win rate of 82.4% over 500 games. Through self-play training, the system demonstrated good learning capabilities and effective strategy improvement. This research provides a new solution for handling board games with randomness and offers a reference framework for the development of similar game systems.