Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. those with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey has been published in 2012. Contributions that appeared since its release are of particular interest for this review.
翻译:蒙特卡洛树搜索(MCTS)是设计游戏游戏机器人或解决相继决定问题的有力方法。该方法依靠明智的树木搜索,平衡勘探和开发。MCTS以模拟和储存行动统计数据的形式进行随机抽样,以便在随后的每次迭代中作出更富于教育的选择。该方法已成为更复杂的游戏(如具有高分流系数或实时分流系数的游戏)以及各种实用领域(如运输、时间安排或安全)的有效 MCTS应用,往往需要根据问题进行修改或与其他技术整合。这种特定领域的修改和混合方法是本次调查的主要重点。上次大型的MCTS调查于2012年公布。 自从发布以来,各种贡献对本次审查特别感兴趣。