Does it matter how well I know what you’re thinking? Opponent Modelling in an RTS game
Does it matter how well I know what you’re thinking? Opponent Modelling in an RTS game
复制标题
我对 RTS 游戏中的对手建模有多了解很重要吗？
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
S. Lucas
中科院分区：
文献类型：
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作者：
J. Goodman;S. Lucas
对手建模试图预测对手未来的行动，并且需要在多人游戏中表现良好。关于学习对手模型有很多深入的文献，但关于这些模型必须有多准确才能有用的文献却少之又少。我们研究了蒙特卡洛树搜索 (MCTS) 和滚动地平线进化算法 (RHEA) 对简单实时策略游戏中对手建模准确性的敏感性。我们发现，在这个领域，RHEA 对对手模型的准确性比 MCTS 更敏感。即使模型不准确，MCTS 通常也会表现更好，但这会降低 RHEA 的性能。我们表明，面对未知的对手和低计算预算，最好不要使用 RHEA 的任何显式模型，并在树内对对手的行为进行建模，作为 MCTS 算法的一部分。
Opponent Modelling tries to predict the future actions of opponents, and is required to perform well in multiplayer 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.