自玩训练一般格斗游戏AI

Yoshina Takano, Hideyasu Inoue, R. Thawonmas, Tomohiro Harada
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引用次数: 4

摘要

在本文中,我们从自玩游戏中训练一个通用的格斗游戏AI,以胜过一个看不见的对手AI。据报道,使用深度Q网络(DQN)的人工智能可以超越训练伙伴。然而,根据我们的经验,DQN AI并不总是优于新对手。通过学习自玩,我们克服了这个缺点,同时保持了DQN AI的优点。我们的实验结果表明,使用具有不同行为的多种人工智能作为训练伙伴更为有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-Play for Training General Fighting Game AI
In this paper, we train a general fighting game AI from self-play games to outperform an unseen opponent AI. It has been reported that an AI using Deep Q Network (DQN) can outperform the training partner. However, according to our experience, the DQN AI is not always superior to a new opponent, unseen before. By learning from self-play, we overcome this drawback while maintaining the DQN AI’s strong points. Our experimental results show that it is more effective to use a variety of AIs with different behaviors as training partners.
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