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Counter Example for Q-Bucket-Brigade Under Prediction Problem

  • Conference paper
Learning Classifier Systems (IWLCS 2003, IWLCS 2004, IWLCS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4399))

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Abstract

Aiming at clarifying the convergence or divergence conditions for Learning Classifier System (LCS), this paper explores: (1) an extreme condition where the reinforcement process of LCS diverges; and (2) methods to avoid such divergence. Based on our previous work that showed equivalence between LCS’s reinforcement process and Reinforcement Learning (RL) with Function approximation (FA) method, we present a counter example for LCS with the Q-bucket-brigade based on the 11-state star problem, a counter example originally proposed to show the divergence of Q-learning with linear FA. Furthermore, the empirical results applying the counter example to LCS verified the results predicted from the theory: (1) LCS with the Q-bucket-brigade diverged under prediction problems, where the action selection policy was fixed; and (2) such divergence was avoided by using the implicit-bucket-brigade or applying residual gradient algorithm to the Q-bucket-brigade.

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Authors and Affiliations

  1. National Institute of Information and Communication Technology,  

    Atsushi Wada

  2. ATR Cognitive Information Science Laboratories, 2-2-2 Hikaridai, “Keihanna Science City” Kyoto 619-0288, Japan

    Atsushi Wada

  3. Department of Human Communication, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofushi, Tokyo 182-8585, Japan

    Keiki Takadama

  4. Faculty of Engineering, Doshisha University, 1-3 Miyakodani, Tatara, Kyotanabe, Kyoto 610-0321, Japan

    Katsunori Shimohara

Authors
  1. Atsushi Wada
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  2. Keiki Takadama
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  3. Katsunori Shimohara
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Editor information

Tim Kovacs Xavier Llorà Keiki Takadama Pier Luca Lanzi Wolfgang Stolzmann Stewart W. Wilson

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Wada, A., Takadama, K., Shimohara, K. (2007). Counter Example for Q-Bucket-Brigade Under Prediction Problem. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS IWLCS IWLCS 2003 2004 2005. Lecture Notes in Computer Science(), vol 4399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71231-2_10

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  • DOI: https://doi.org/10.1007/978-3-540-71231-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71230-5

  • Online ISBN: 978-3-540-71231-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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