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Adaptive aggregation for reinforcement learning in average reward Markov decision processes

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  • Ronald Ortner

Abstract

We present an algorithm which aggregates online when learning to behave optimally in an average reward Markov decision process. The algorithm is based on the reinforcement learning algorithm UCRL and uses confidence intervals for aggregating the state space. We derive bounds on the regret our algorithm suffers with respect to an optimal policy. These bounds are only slightly worse than the original bounds for UCRL. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Ronald Ortner, 2013. "Adaptive aggregation for reinforcement learning in average reward Markov decision processes," Annals of Operations Research, Springer, vol. 208(1), pages 321-336, September.
    • Handle: RePEc:spr:annopr:v:208:y:2013:i:1:p:321-336:10.1007/s10479-012-1064-y
    DOI: 10.1007/s10479-012-1064-y
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    References listed on IDEAS

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    1. Hyeong Soo Chang & Michael C. Fu & Jiaqiao Hu & Steven I. Marcus, 2005. "An Adaptive Sampling Algorithm for Solving Markov Decision Processes," Operations Research, INFORMS, vol. 53(1), pages 126-139, February.
    2. Apostolos N. Burnetas & Michael N. Katehakis, 1997. "Optimal Adaptive Policies for Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 22(1), pages 222-255, February.
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