在实时第一人称射击电脑游戏中创建自主自适应代理

D. Wang, A. Tan
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引用次数: 44

摘要

游戏是评估AI方法的良好测试平台。近年来,除了传统的桌面游戏或纸牌游戏之外,人们对实时电脑游戏进行了大量的研究。本文阐述了我们如何通过使用FALCON(一种执行强化学习的自组织神经网络)来创建代理,并玩一款著名的第一人称射击电脑游戏《虚幻竞技场》。用于学习的奖励要么从游戏环境中获得,要么使用时间差异学习方案估计。这样,agent就可以在没有任何指导和干预的情况下获得适当的策略,并发现不同武器的有效性。实验结果表明,我们的智能体在实时玩游戏的过程中,能够有效而恰当地从零开始学习。此外,由于保留了先前学习的知识,我们的智能体能够在相对较短的时间内适应不同地图上的不同对手。
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Creating Autonomous Adaptive Agents in a Real-Time First-Person Shooter Computer Game
Games are good test-beds to evaluate AI methodologies. In recent years, there has been a vast amount of research dealing with real-time computer games other than the traditional board games or card games. This paper illustrates how we create agents by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first-person shooter computer game called Unreal Tournament. Rewards used for learning are either obtained from the game environment or estimated using the temporal difference learning scheme. In this way, the agents are able to acquire proper strategies and discover the effectiveness of different weapons without any guidance or intervention. The experimental results show that our agents learn effectively and appropriately from scratch while playing the game in real-time. Moreover, with the previously learned knowledge retained, our agent is able to adapt to a different opponent in a different map within a relatively short period of time.
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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.
期刊最新文献
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