On Online Learning in Kernelized Markov Decision Processes

Sayak Ray Chowdhury; Aditya Gopalan

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arXiv · 1911.01871

On Online Learning in Kernelized Markov Decision Processes

Sayak Ray Chowdhury·Aditya Gopalan

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Abstract

We develop algorithms with low regret for learning episodic Markov decision processes based on kernel approximation techniques. The algorithms are based on both the Upper Confidence Bound (UCB) as well as Posterior or Thompson Sampling (PSRL) philosophies, and work in the general setting of continuous state and action spaces when the true unknown transition dynamics are assumed to have smoothness induced by an appropriate Reproducing Kernel Hilbert Space (RKHS).

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Sayak Ray Chowdhury, Aditya Gopalan. 2019-11-04. On Online Learning in Kernelized Markov Decision Processes. https://arxiv.org/abs/1911.01871

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