Board games have long served as complex decision-making benchmarks in artificial intelligence. In this field, search-based reinforcement learning methods such as AlphaZero have achieved remarkable success. However, their significant computational demands have been pointed out as barriers to their reproducibility. In this study, we propose a model-free reinforcement learning algorithm designed for board games to achieve more efficient learning. To validate the efficiency of the proposed method, we conducted comprehensive experiments on five board games: Animal Shogi, Gardner Chess, Go, Hex, and Othello. The results demonstrate that the proposed method achieves more efficient learning than existing methods across these environments. In addition, our extensive ablation study shows the importance of core techniques used in the proposed method. We believe that our efficient algorithm shows the potential of model-free reinforcement learning in domains traditionally dominated by search-based methods.

翻译：棋盘游戏长期以来一直是人工智能领域中复杂的决策制定基准。在该领域，基于搜索的强化学习方法（如AlphaZero）已取得显著成功。然而，其巨大的计算需求已被指出是阻碍其可复现性的主要障碍。在本研究中，我们提出了一种专为棋盘游戏设计的无模型强化学习算法，以实现更高效的学习。为验证所提方法的效率，我们在五种棋盘游戏上进行了全面实验：动物将棋、加德纳国际象棋、围棋、六边形棋和黑白棋。实验结果表明，所提方法在这些环境中均实现了比现有方法更高效的学习。此外，我们广泛的消融研究揭示了所提方法中核心技术的重要性。我们相信，我们提出的高效算法展现了无模型强化学习在传统上由基于搜索方法主导的领域中的潜力。