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MCTS-Minimax Hybrids

机译：MCTS-Minimax混合动力车

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摘要

Monte Carlo tree search (MCTS) is a sampling-based search algorithm that is state of the art in a variety of games. In many domains, its Monte Carlo rollouts of entire games give it a strategic advantage over traditional depth-limited minimax search with pruning. These rollouts can often detect long-term consequences of moves, freeing the programmer from having to capture these consequences in a heuristic evaluation function. But due to its highly selective tree, MCTS runs a higher risk than full-width minimax search of missing individual moves and falling into traps in tactical situations. This paper proposes MCTS-minimax hybrids that integrate shallow minimax searches into the MCTS framework. Three approaches are outlined, using minimax in the selection/expansion phase, the rollout phase, and the backpropagation phase of MCTS. Without assuming domain knowledge in the form of evaluation functions, these hybrid algorithms are a first step towards combining the strategic strength of MCTS and the tactical strength of minimax. We investigate their effectiveness in the test domains of Connect-4, Breakthrough, Othello, and Catch the Lion, and relate this performance to the tacticality of the domains.
机译：蒙特卡罗树搜索（MCTS）是一种基于采样的搜索算法，在各种游戏中都是最先进的。在许多领域中，其整个游戏的“蒙特卡洛”推广在战略意义上超过了传统的限深minimax修剪算法。这些部署通常可以检测到移动的长期后果，从而使程序员不必在启发式评估功能中捕获这些后果。但是由于具有高度选择性的树，MCTS的风险要比全幅minimax搜索丢失的单个动作和在战术情况下陷入陷阱的风险更高。本文提出了MCTS-minimax混合体，该混合体将浅层minimax搜索集成到MCTS框架中。概述了三种方法，在MCTS的选择/扩展阶段，推出阶段和反向传播阶段使用minimax。在不假设评估功能形式的领域知识的情况下，这些混合算法是将MCTS的战略实力和minimax战术实力相结合的第一步。我们调查了它们在Connect-4，Breakthrough，Othello和Catch the Lion的测试域中的有效性，并将此性能与域的战术性相关联。

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