Reinforcement Learning in Video Games Using Nearest Neighbor Interpolation and Metric Learning

Q2 Computer Science
Matthew S. Emigh, E. Kriminger, A. Brockmeier, J. Príncipe, P. Pardalos
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引用次数: 25

Abstract

Reinforcement learning (RL) has had mixed success when applied to games. Large state spaces and the curse of dimensionality have limited the ability for RL techniques to learn to play complex games in a reasonable length of time. We discuss a modification of Q-learning to use nearest neighbor states to exploit previous experience in the early stages of learning. A weighting on the state features is learned using metric learning techniques, such that neighboring states represent similar game situations. Our method is tested on the arcade game Frogger, and it is shown that some of the effects of the curse of dimensionality can be mitigated.
基于最近邻插值和度量学习的电子游戏强化学习
强化学习(RL)在应用于游戏时取得了不同程度的成功。大的状态空间和维度的诅咒限制了强化学习技术在合理时间内学习复杂游戏的能力。我们讨论了对q学习的一种修改,即在学习的早期阶段使用最近邻状态来利用先前的经验。使用度量学习技术来学习状态特征的权重,这样相邻的状态就代表了类似的游戏情境。我们的方法在街机游戏《青蛙过河》上进行了测试,结果表明,维度诅咒的一些影响是可以减轻的。
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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