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arXiv · 2007.05078

A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces

Omar Darwiche Domingues·Pierre Ménard·Matteo Pirotta·Emilie Kaufmann·Michal Valko
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Abstract

In this work, we propose KeRNS: an algorithm for episodic reinforcement learning in non-stationary Markov Decision Processes (MDPs) whose state-action set is endowed with a metric. Using a non-parametric model of the MDP built with time-dependent kernels, we prove a regret bound that scales with the covering dimension of the state-action space and the total variation of the MDP with time, which quantifies its level of non-stationarity. Our method generalizes previous approaches based on sliding windows and exponential discounting used to handle changing environments. We further propose a practical implementation of KeRNS, we analyze its regret and validate it experimentally.

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Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko. 2020-07-09. A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. https://arxiv.org/abs/2007.05078

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