Regret Bounds for Kernel-Based Reinforcement Learning

12 Apr 2020Omar Darwiche DominguesPierre MénardMatteo PirottaEmilie KaufmannMichal Valko

We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning problems whose state-action space is endowed with a metric. We introduce Kernel-UCBVI, a model-based optimistic algorithm that leverages the smoothness of the MDP and a non-parametric kernel estimator of the rewards and transitions to efficiently balance exploration and exploitation... (read more)

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