CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 深度强化学习
Learning battles in ViZDoom via deep reinforcement learning
Kun Shao1,2; Dongbin Zhao1,2; Nannan Li1,2; Yuanheng Zhu1,2
2018-10
Conference NameIEEE Conference on Computational Intelligence and Games
Conference Date2018-08
Conference PlaceMaastricht, The Netherlands
Abstract

First-person shooter (FPS) video games play an important role in game artificial intelligence (AI). In this paper, we present an effective deep reinforcement learning (DRL) method to learn battles in ViZDoom. Our approach utilizes the actorcritic with Kronecker-factored trust region (ACKTR), a sampleefficient and computationally inexpensive DRL method. We train our ACKTR agents in two battle scenarios, and compare with the advantage actor-critic (A2C) baseline agent. The experimental
results demonstrate that DRL methods successfully teach agents to battle in these scenarios. In addition, the ACKTR agents significantly outperform the A2C agents in terms of all the metrics by a significant margin.

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KeywordReinforcement Learning, Deep Learning, Game Ai
MOST Discipline Catalogue工学
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23364
Collection复杂系统管理与控制国家重点实验室_深度强化学习
Corresponding AuthorDongbin Zhao
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Kun Shao,Dongbin Zhao,Nannan Li,et al. Learning battles in ViZDoom via deep reinforcement learning[C],2018.
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