论文标题
基于多样性的深度强化学习对格斗游戏的多维难度
Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI
论文作者
论文摘要
在格斗游戏中，具有相同技能水平的个人玩家经常通过游戏玩法表现出彼此的独特策略。尽管如此，大多数用于格斗游戏的人工智能代理人对每个难度的“级别”都有一个策略。为了使AI对手更像人性化，我们理想地希望在每个困难级别上看到多种不同的策略，这一概念我们称为“多维”难度。在本文中，我们介绍了一种基于多样性的深入强化学习方法，以产生一组使用各种策略的类似困难的代理。我们发现这种方法的表现优于基线，该基线在多样性和绩效方面都具有专门的，人为的奖励功能。
In fighting games, individual players of the same skill level often exhibit distinct strategies from one another through their gameplay. Despite this, the majority of AI agents for fighting games have only a single strategy for each "level" of difficulty. To make AI opponents more human-like, we'd ideally like to see multiple different strategies at each level of difficulty, a concept we refer to as "multidimensional" difficulty. In this paper, we introduce a diversity-based deep reinforcement learning approach for generating a set of agents of similar difficulty that utilize diverse strategies. We find this approach outperforms a baseline trained with specialized, human-authored reward functions in both diversity and performance.
