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Short Talk 3 - A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces
Omar Darwiche Domingues

Fri Jul 17 11:50 AM -- 12:05 PM (PDT) @

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.

Omar Darwiche Domingues, Pierre MENARD, Matteo Pirotta, Emilie Kaufmann, Michal Valko

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Omar Darwiche Domingues (Inria)

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