Cited By
View all- Kim MKim JAhn C(2023)Evolving population method for real-time reinforcement learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120493229:PAOnline publication date: 1-Nov-2023
Genetic Optimizing Method for Real-time Monte Carlo Tree Search Problem
Pages 50 - 51
Abstract
Monte Carlo Tree Search is one of the best algorithms for solving board game problems. However, Monte Carlo Tree Search is not suitable for real-time game problem because the problems have uncertainty of opponent’s action and a lot of simulation when determining behavior. We propose a Genetic Optimizing Method to solving the problems encountered when applying Monte Carlo Tree Search to real-time games. Our method helps solve the dilemma of Real-time Monte Carlo Tree Search between simulation and the number of branching factors by utilizing genetic algorithms. Finally, we applied our method to the Real-time Fighting Game to verify its performance.
References
[1]
Cameron Browne 2012. A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games 4, 1(2012), 1–43.
[2]
Man-Je Kim, Kim Jun Suk, Kim Sungjin, James, Kim Min-jung, and Ahn Chang Wook. 2020. Genetic State-Grouping Algorithm for Deep Reinforcement Learning. Expert Systems with Applications 161, 113695 (2020).
[3]
Yoshida Shubu, Ishihara Makoto, Miyazaki Taichi, Nakagawa Yuto, Harada Tomohiro, and Thawonmas Ruck. 2016. Application of Monte-Carlo tree search in a fighting game AI. In 2016 IEEE 5th Global Conference on Consumer Electronics. IEEE, 1–2.
[4]
David Silver 2016. Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature 529, 7587 (2016), 484–489.
- Genetic Optimizing Method for Real-time Monte Carlo Tree Search Problem
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Published In
September 2020
491 pages
ISBN:9781450389259
DOI:10.1145/3426020
Copyright © 2020 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 November 2021
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- Short-paper
- Research
- Refereed limited
Funding Sources
- National Research Foundation ofKorea (NRF) grant
- National Research Foundation of Korea (NRF) grant fund
- National Research Foundation of Korea (NRF) grant
Conference
SMA 2020
SMA 2020: The 9th International Conference on Smart Media and Applications
September 17 - 19, 2020
Jeju, Republic of Korea
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Cited By
View all- Kim MKim JAhn C(2023)Evolving population method for real-time reinforcement learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120493229:PAOnline publication date: 1-Nov-2023
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