基于隐式Minimax备份的启发式蒙特卡罗树搜索
Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups
摘要：
蒙特卡罗树搜索（MCTS）提高了围棋、十六进制和一般游戏等领域的游戏引擎的性能。MCTS已被证明在难以获得良好启发式评估的游戏中优于经典的alpha-beta搜索。近年来，在MCTS中结合传统的极大极小搜索的思想已经被证明在某些领域是有利的，例如行动线，亚马逊和突破。在本文中，我们提出了一种新的方法来使用启发式评估，以指导MCTS搜索存储的两个来源的信息，估计的胜率和启发式评估，分别。而不是使用启发式的评估，以取代播放，我们的技术支持他们隐式在MCTS模拟。这些极小极大值然后用于指导未来的模拟。我们表明，使用隐式极大极小备份导致更强的发挥性能在卡拉，突破，和行动线。
英文摘要：
Monte Carlo Tree Search (MCTS) has improved the performance of game engines in domains such as Go, Hex, and general game playing. MCTS has been shown to outperform classic alpha-beta search in games where good heuristic evaluations are difficult to obtain. In recent years, combining ideas from traditional minimax search in MCTS has been shown to be advantageous in some domains, such as Lines of Action, Amazons, and Breakthrough. In this paper, we propose a new way to use heuristic evaluations to guide the MCTS search by storing the two sources of information, estimated win rates and heuristic evaluations, separately. Rather than using the heuristic evaluations to replace the playouts, our technique backs them up implicitly during the MCTS simulations. These minimax values are then used to guide future simulations. We show that using implicit minimax backups leads to stronger play performance in Kalah, Breakthrough, and Lines of Action.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
