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http://dx.doi.org/10.7583/JKGS.2020.20.6.23

Improvement of the Gonu game using progressive deepening in reinforcement learning  

Shin, YongWoo (Division of Creative Convergence Education, Dong-Ah Institute of Media and Arts)
Publication Information
Journal of Korea Game Society / v.20, no.6, 2020 , pp. 23-30 More about this Journal
Abstract
There are many cases in the game. So, Game have to learn a lot. This paper uses reinforcement learning to improve the learning speed. However, because reinforcement learning has many cases, it slows down early in learning. So, the speed of learning was improved by using the minimax algorithm. In order to compare the improved performance, a Gonu game was produced and tested. As for the experimental results, the win rate was high, but the result of a tie occurred. The game tree was further explored using progressive deepening to reduce tie cases and win rate has improved by about 75%.
Keywords
Gonu game; Minimax algorithm; Q Learning; Learning speed; Progressive deepening;
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