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Robust Bayesian Reinforcement Learning through Tight Lower Bounds

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Recent Advances in Reinforcement Learning (EWRL 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7188))

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Abstract

In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinforcement learning problems. While utility bounds are known to exist for this problem, so far none of them were particularly tight. In this paper, we show how to efficiently calculate a lower bound, which corresponds to the utility of a near-optimal memoryless policy for the decision problem, which is generally different from both the Bayes-optimal policy and the policy which is optimal for the expected MDP under the current belief. We then show how these can be applied to obtain robust exploration policies in a Bayesian reinforcement learning setting.

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Author information

Authors and Affiliations

  1. EPFL, Lausanne, Switzerland

    Christos Dimitrakakis

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  1. Christos Dimitrakakis
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Editors and Affiliations

  1. NICTA and the Australian National University, 7 London Circuit, ACT 2601, Canberra, Australia

    Scott Sanner

  2. Research School of Computer Science, Australian National University, ACT 0200, Canberra, Australia

    Marcus Hutter

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Dimitrakakis, C. (2012). Robust Bayesian Reinforcement Learning through Tight Lower Bounds. In: Sanner, S., Hutter, M. (eds) Recent Advances in Reinforcement Learning. EWRL 2011. Lecture Notes in Computer Science(), vol 7188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29946-9_19

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  • DOI: https://doi.org/10.1007/978-3-642-29946-9_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29945-2

  • Online ISBN: 978-3-642-29946-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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