Reinforcement learning with echo state networks

István Szita, Viktor Gyenes, András Lorincz

Research output: Conference contribution

26 Citations (Scopus)

Abstract

Function approximators are often used in reinforcement learning tasks with large or continuous state spaces. Artificial neural networks, among them recurrent neural networks are popular function approximators, especially in tasks where some kind of of memory is needed, like in real-world partially observable scenarios. However, convergence guarantees for such methods are rarely available. Here, we propose a method using a class of novel RNNs, the echo state networks. Proof of convergence to a bounded region is provided for k-order Markov decision processes. Runs on POMDPs were performed to test and illustrate the working of the architecture.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
PublisherSpringer Verlag
Pages830-839
Number of pages10
ISBN (Print)3540386254, 9783540386254
DOIs
Publication statusPublished - jan. 1 2006
Event16th International Conference on Artificial Neural Networks, ICANN 2006 - Athens, Greece
Duration: szept. 10 2006szept. 14 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4131 LNCS - I
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Artificial Neural Networks, ICANN 2006
CountryGreece
CityAthens
Period9/10/069/14/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Szita, I., Gyenes, V., & Lorincz, A. (2006). Reinforcement learning with echo state networks. In Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings (pp. 830-839). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4131 LNCS - I). Springer Verlag. https://doi.org/10.1007/11840817_86