Volume 5 Number 12 (Dec. 2010)
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JSW 2010 Vol.5(12): 1327-1333 ISSN: 1796-217X
doi: 10.4304/jsw.5.12.1327-1333

Applying Reinforcement Learning for the AI in a Tank-Battle Game

Yung-Ping Fang, I-Hsien Ting
Department of Information Management National University of Kaohsiung, No. 700, Kaohsiung University Road, Kaohsiung, 811 Taiwan

Abstract—Reinforcement learning is an unsupervised machine learning method in the field of Artificial Intelligence and offers high performance in simulating the thinking ability of a human. However, it requires a trialand- error process to achieve this goal. In the research field of game AIs, it is a good approach that can give the nonplayer- characters (NPCs) in digital games more human-like qualities. In this paper, we try to build a Tank-battle computer game and use the methodology of reinforcement learning for the NPCs (the tanks). The goal of this paper is to make this game become more interesting due to the enhanced interactions with the more intelligent NPCs.

Index Terms—artificial intelligence, reinforcement learning.


Cite: Yung-Ping Fang, I-Hsien Ting, "Applying Reinforcement Learning for the AI in a Tank-Battle Game," Journal of Software vol. 5, no. 12, pp. 1327-1333, 2010.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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