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Fuzzy Epoch-Incremental Reinforcement Learning Algorithm

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7267))

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Abstract

The new epoch-incremental reinforcement learning algorithm with fuzzy approximation of action-value function is developed. This algorithm is practically tested in the control of the mobile robot which realizes goal seeking behavior. The obtained results are compared with results of fuzzy version of reinforcement learning algorithms, such as Q(0)-learning, Q(λ)-learning, Dyna-learning and prioritized sweeping. The adaptation of the fuzzy approximator to the model based reinforcement learning algorithms is also proposed.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, W. Pola 2, 35-959, Rzeszow, Poland

    Roman Zajdel

Authors
  1. Roman Zajdel
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Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Zajdel, R. (2012). Fuzzy Epoch-Incremental Reinforcement Learning Algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_42

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  • DOI: https://doi.org/10.1007/978-3-642-29347-4_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29346-7

  • Online ISBN: 978-3-642-29347-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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