Corpus ID: 219955936

Provably adaptive reinforcement learning in metric spaces

  title={Provably adaptive reinforcement learning in metric spaces},
  author={Tongyi Cao and A. Krishnamurthy},
We study reinforcement learning in continuous state and action spaces endowed with a metric. We provide a refined analysis of the algorithm of Sinclair, Banerjee, and Yu (2019) and show that its regret scales with the \emph{zooming dimension} of the instance. This parameter, which originates in the bandit literature, captures the size of the subsets of near optimal actions and is always smaller than the covering dimension used in previous analyses. As such, our results are the first provably… Expand
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Adaptive aggregation for reinforcement learning in average reward Markov decision processes
  • R. Ortner
  • Mathematics, Computer Science
  • Ann. Oper. Res.
  • 2013
An algorithm which aggregates online when learning to behave optimally in an average reward Markov decision process and derives bounds on the regret this algorithm suffers with respect to an optimal policy is presented. Expand