Bayesian Reinforcement Learning with Exploration

  • Tor Lattimore
  • Marcus Hutter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8776)

Abstract

We consider a general reinforcement learning problem and show that carefully combining the Bayesian optimal policy and an exploring policy leads to minimax sample-complexity bounds in a very general class of (history-based) environments. We also prove lower bounds and show that the new algorithm displays adaptive behaviour when the environment is easier than worst-case.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tor Lattimore
    • 1
  • Marcus Hutter
    • 2
  1. 1.University of AlbertaCanada
  2. 2.Australian National UniversityAustralia

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