An improvement of the learning speed through Improved Reinforcement Learning on Jul-Gonu Game

Yong-Woo Shin; Tae-Choong Chung

An improvement of the learning speed through Improved Reinforcement Learning on Jul-Gonu Game

Yong-Woo Shin, Tae-Choong Chung, Journal of Internet Computing and Services, Vol. 10, No. 3, pp. 9-16, Jun. 2009
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Keywords: Reinforcement Learning, Jul-Gonu, Gonu, Learning Speed, Game

Abstract

It takes quite amount of time to study a board game because there are many game characters and different stages are exist for board games. Also, the opponent is not just a single character that means it is not one on one game, but group vs. group. That is why strategy is needed, and therefore applying optimum learning is a must. This paper used reinforcement learning algorithm for board characters to learn, and so they can move intelligently. If there were equal result that both are considered to be best ones during the course of learning stage, Heuristic which utilizes learning of problem area of Jul-Gonu was used to improve the speed of learning. To compare a normal character to an improved one, a board game was created, and then they fought against each other. As a result, improved character's ability was far more improved on learning speed.

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Cite this article

[APA Style]

Shin, Y. & Chung, T. (2009). An improvement of the learning speed through Improved Reinforcement Learning on Jul-Gonu Game. Journal of Internet Computing and Services, 10(3), 9-16.

[IEEE Style]

Y. Shin and T. Chung, "An improvement of the learning speed through Improved Reinforcement Learning on Jul-Gonu Game," Journal of Internet Computing and Services, vol. 10, no. 3, pp. 9-16, 2009.

[ACM Style]

Yong-Woo Shin and Tae-Choong Chung. 2009. An improvement of the learning speed through Improved Reinforcement Learning on Jul-Gonu Game. Journal of Internet Computing and Services, 10, 3, (2009), 9-16.