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Gemini in RoboCup-2000

  • Masayuki Ohta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2019)

Abstract

We implemented “Gemini” a client program for the SoccerServer. The objective of this program is testing a lot of learning methods on multi-agent environments. In the current implementation,Gemini can select the most effective strategy for an enemy, using reinforcement learning. Furthermore, we are trying to implement a meta-level learning, which turn each learning function on or off according to whether the learning succeed or not.

References

  1. 1.
    Kaelbling L. P., Littman M. L. and Moore A. W. “Reinforcement Learning: A Survey” Journal of Artificial Intelligence Research 4,pages 237–285 1996.Google Scholar
  2. 2.
    Kimura H., Yamamura M. and Kobayashi S. “Reinforcement Learning in Partially Observable Markov Decision Processes: A Stochastic Gradient Method” Journal of Japanese Society for Artificial Intelligence, Vol.11, No.5 pages 761–768 1996Google Scholar
  3. 3.
    Itsuki Noda, Hitoshi Matsubara, Kazuo Hiraki and Ian Frank. Soccer Server: A Tool for Research on Multiagent Systems. Applied Artificial Intelligence, Vol.12, pages 233–250,1998.CrossRefGoogle Scholar
  4. 4.
    M. Ohta and T. Ando “Cooperative Reward in Reinforcement Learning” Proc. of 3rd JSAI RoboMech Symposia pages 7–11 April 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Masayuki Ohta
    • 1
  1. 1.Tokyo Institute of TechnologyJapan

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