A kernel-based approach to non-stationary reinforcement learning in metric spaces
Résumé
In this work, we propose KeRNS: an algorithm for episodic reinforcement learning in nonstationary 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.
Domaines
Machine Learning [stat.ML]Origine | Fichiers produits par l'(les) auteur(s) |
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