Hybrid of evolution and reinforcement learning for othello players

Kyung Joong Kim, Heejin Choi, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

Although the reinforcement learning and evolutionary algorithm show good results in board evaluation optimization, the hybrid of both approaches is rarely addressed in the literature. In this paper, the evolutionary algorithm is boosted using resources from the reinforcement learning. 1) The initialization of initial population using solution optimized by temporal difference learning 2) Exploitation of domain knowledge extracted from reinforcement learning. Experiments on Othello game strategies show that the proposed methods can effectively search the solution space and improve the performance.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE Symposium on Computational Intelligence and Games, CIG 2007
Pages203-209
Number of pages7
DOIs
Publication statusPublished - 2007 Sep 25
Event2007 IEEE Symposium on Computational Intelligence and Games, CIG 2007 - Honolulu, HI, United States
Duration: 2007 Apr 12007 Apr 5

Publication series

NameProceedings of the 2007 IEEE Symposium on Computational Intelligence and Games, CIG 2007

Other

Other2007 IEEE Symposium on Computational Intelligence and Games, CIG 2007
CountryUnited States
CityHonolulu, HI
Period07/4/107/4/5

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Theoretical Computer Science

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  • Cite this

    Kim, K. J., Choi, H., & Cho, S. B. (2007). Hybrid of evolution and reinforcement learning for othello players. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Games, CIG 2007 (pp. 203-209). [4219044] (Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Games, CIG 2007). https://doi.org/10.1109/CIG.2007.368099