Monte Carlo tree search for maintaining the global game graph
The AlphaGo series algorithms have significantly advanced artificial intelligence in board games by employing neural networks with learning value and policy networks to guide the Monte Carlo Tree Search method.Recent research results indicate replacing Monte Carlo Tree Search with Monte Carlo Graph Search can further enhance the program' s search efficiency.On this basis, this paper employs a novel method known as the Monte Carlo graph search for maintaining the global game graph.This method, by maintaining a global game graph, utilizes the expired node deletion algorithm to eliminate nodes and edges without value.Additionally, it employs measures such as reasoning calculations during the opponent' s turn, enhancing the program's search efficiency.Our experiment on Hex demonstrates this method, under limited computing resources, exhibits an enhanced winning rate compared to alternative search strategies.
AlphaGo series algorithmscomputer-based gameMonte Carlo graph searchcomputational resources
国家科技期刊平台
NSTL
万方数据
