基于将军搜索的自我游戏评价函数学习

T. Nakayashiki, Tomoyuki Kaneko
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引用次数: 3

摘要

如AlphaGo、AlphaGo Zero和AlphaZero所示,强化学习在围棋、国际象棋和棋棋的评估函数(或价值网络)的学习中是有效的。在他们的训练中,两个程序并行重复;具有当前评估功能的自我游戏,以及通过使用最近自我游戏产生的游戏记录来改进评估功能。虽然AlphaGo、AlphaGo Zero和AlphaZero已经取得了超人类的表现,但这种方法需要巨大的计算资源。为了解决这一问题,本文提出了将死求解器。我们发现,通过自我游戏记录的质量,这一小小的改进显著提高了我们在迷你hogi中的实验效率。应该注意的是,我们的方法仍然不需要人类对目标领域的知识,尽管将军求解器的实现依赖于领域。
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Learning of Evaluation Functions via Self-Play Enhanced by Checkmate Search
As shown in AlphaGo, AlphaGo Zero, and AlphaZero, reinforcement learning is effective in learning of evaluation functions (or value networks) in Go, Chess and Shogi. In their training, two procedures are repeated in parallel; self-play with a current evaluation function and improvement of the evaluation function by using game records yielded by recent self-play. Although AlphaGo, AlphaGo Zero, and AlphaZero have achieved super human performance, the method requires enormous computation resources. To alleviate the problem, this paper proposes to incorporate a checkmate solver in self-play. We show that this small enhancement dramatically improves the efficiency of our experiments in Minishogi, via the quality of game records in self-play. It should be noted that our method is still free from human knowledge about a target domain, though the implementation of checkmate solvers is domain dependent.
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