多目标蒙特卡罗树搜索中的在线和离线学习

Diego Perez Liebana, Spyridon Samothrakis, S. Lucas
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引用次数: 11

摘要

多目标优化通常应用于制造、工程或金融领域,但对游戏研究影响不大。然而,将其应用于这一研究领域可能会产生有趣的结果,特别是对于那些复杂或足够长的游戏,即长期规划并非微不足道,或者良好的游戏水平取决于游戏中多种策略的平衡。提出了一种新的基于蒙特卡罗树搜索的多目标算法。该算法在两种不同的场景下进行了测试,其学习能力以在线和离线方式进行了测量。此外,还将其与先进的多目标进化算法(NSGA-II)和先前发表的多目标MCTS算法进行了比较。结果表明,本文提出的算法与其他技术相比具有相似或更好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online and offline learning in multi-objective Monte Carlo Tree Search
Multi-Objective optimization has traditionally been applied to manufacturing, engineering or finance, with little impact in games research. However, its application to this field of study may provide interesting results, especially for games that are complex or long enough that long-term planning is not trivial and/or a good level of play depends on balancing several strategies within the game. This paper proposes a new Multi-Objective algorithm based on Monte Carlo Tree Search (MCTS). The algorithm is tested in two different scenarios and its learning capabilities are measured in an online and offline fashion. Additionally, it is compared with a state of the art multi-objective evolutionary algorithm (NSGA-II) and with a previously published Multi-Objective MCTS algorithm. The results show that our proposed algorithm provides similar or better results than other techniques.
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