论文标题
我知道你在想什么，这有多好吗？ RTS游戏中的对手建模
Does it matter how well I know what you're thinking? Opponent Modelling in an RTS game
论文作者
论文摘要
对手建模试图预测对手的未来行动，并且需要在多玩家游戏中表现良好。有关于学习对手模型的深刻文献，但是关于这种模型必须有用的准确程度要少得多。我们研究了蒙特卡洛树搜索（MCT）和滚动范围进化算法（RHEA）对对手在简单的实时策略游戏中对对手建模的准确性的敏感性。我们发现，在这个领域，与MCT相比，RheA对对手模型的准确性更为敏感。即使使用不准确的模型，MCT通常会做得更好，而这会降低Rhea的性能。我们表明，面对一个未知的对手和低计算预算，最好不要使用RHEA使用任何明确的模型，并将对手在树内的动作建模为MCTS算法的一部分。
Opponent Modelling tries to predict the future actions of opponents, and is required to perform well in multi-player games. There is a deep literature on learning an opponent model, but much less on how accurate such models must be to be useful. We investigate the sensitivity of Monte Carlo Tree Search (MCTS) and a Rolling Horizon Evolutionary Algorithm (RHEA) to the accuracy of their modelling of the opponent in a simple Real-Time Strategy game. We find that in this domain RHEA is much more sensitive to the accuracy of an opponent model than MCTS. MCTS generally does better even with an inaccurate model, while this will degrade RHEA's performance. We show that faced with an unknown opponent and a low computational budget it is better not to use any explicit model with RHEA, and to model the opponent's actions within the tree as part of the MCTS algorithm.
