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Reinforcement Learning of Intelligent Characters in Fighting Action Games

  • Byeong Heon Cho
  • Sung Hoon Jung
  • Kwang-Hyun Shim
  • Yeong Rak Seong
  • Ha Ryoung Oh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4161)

Abstract

In this paper, we investigate reinforcement learning (RL) of intelligent characters, based on neural network technology, for fighting action games. RL can be either on-policy or off-policy. We apply both schemes to tabula rasa learning and adaptation. The experimental results show that (1) in tabula rasa leaning, off-policy RL outperforms on-policy RL, but (2) in adaptation, on-policy RL outperforms off-policy RL.

Keywords

Reinforcement Learn Output Action Input State Action Game Static Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Cho, B.H., Jung, S.H., Seong, Y.R., Oh, H.R.: Exploiting intelligence in fighting action games using neural networks. IEICE Trans. on Information and Systems (to appear)Google Scholar
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    Cho, B.H., Jung, S.H., Shim, K.-H., Seong, Y.R., Oh, H.R.: Adaptation of intelligent characters to changes of game environments. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 1064–1073. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2006

Authors and Affiliations

  • Byeong Heon Cho
    • 1
  • Sung Hoon Jung
    • 2
  • Kwang-Hyun Shim
    • 1
  • Yeong Rak Seong
    • 3
  • Ha Ryoung Oh
    • 3
  1. 1.Digital Content Research DivisionETRIDaejeonKorea
  2. 2.Dept. of Information and Comm. Eng.Hansung Univ.SeoulKorea
  3. 3.School of Electrical EngineeringKookmin Univ.SeoulKorea

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