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Multiobjective Monte Carlo Tree Search for Real-Time Games

机译：多目标蒙特卡洛树搜索实时游戏

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摘要

Multiobjective optimization has been traditionally a matter of study in domains like engineering or finance, with little impact on games research. However, action-decision based on multiobjective evaluation may be beneficial in order to obtain a high quality level of play. This paper presents a multiobjective Monte Carlo tree search algorithm for planning and control in real-time game domains, those where the time budget to decide the next move to make is close to 40 ms. A comparison is made between the proposed algorithm, a single-objective version of Monte Carlo tree search and a rolling horizon implementation of nondominated sorting evolutionary algorithm II (NSGA-II). Two different benchmarks are employed, deep sea treasure (DST) and the multiobjective physical traveling salesman problem (MO-PTSP). Using the same heuristics on each game, the analysis is focused on how well the algorithms explore the search space. Results show that the algorithm proposed outperforms NSGA-II. Additionally, it is also shown that the algorithm is able to converge to different optimal solutions or the optimal Pareto front (if achieved during search).
机译：传统上，多目标优化一直是工程或金融等领域的研究问题，对游戏研究的影响很小。但是，基于多目标评估的行动决策可能会有益于获得高质量的游戏水平。本文提出了一种用于实时游戏领域中的计划和控制的多目标蒙特卡洛树搜索算法，这些领域中用于决定下一步行动的时间预算接近40毫秒。在提出的算法，蒙特卡洛树搜索的单目标版本与非支配排序进化算法II（NSGA-II）的滚动层实现之间进行了比较。使用两种不同的基准，即深海宝藏（DST）和多目标实物旅行业务员问题（MO-PTSP）。在每个游戏上使用相同的试探法，分析集中在算法探索搜索空间的能力上。结果表明，该算法优于NSGA-II。另外，还表明该算法能够收敛到不同的最优解或最优帕累托前沿（如果在搜索过程中实现）。

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