Robust population coding in free-energy-based reinforcement learning

Makoto Otsuka, Junichiro Yoshimoto, Kenji Doya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We investigate the properties of free-energy-based reinforcement learning using a new experimental platform called the digit floor task. The simulation results showed the robustness of the reinforcement learning method against noise applied in both the training and testing phases. In addition, reward-dependent and reward-invariant representations were found in the distributed activation patterns of hidden units. The representations coded in a distributed fashion persisted even when the number of hidden nodes were varied.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2008 - 18th International Conference, Proceedings
Pages377-386
Number of pages10
EditionPART 1
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event18th International Conference on Artificial Neural Networks, ICANN 2008 - Prague, Czech Republic
Duration: 03-09-200806-09-2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5163 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Artificial Neural Networks, ICANN 2008
Country/TerritoryCzech Republic
CityPrague
Period03-09-0806-09-08

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Robust population coding in free-energy-based reinforcement learning'. Together they form a unique fingerprint.

Cite this