Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities

Aristide Tossou; Debabrota Basu; Christos Dimitrakakis

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arXiv · 1905.12425

Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities

Aristide Tossou·Debabrota Basu·Christos Dimitrakakis

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Abstract

We study model-based reinforcement learning in an unknown finite communicating Markov decision process. We propose a simple algorithm that leverages a variance based confidence interval. We show that the proposed algorithm, UCRL-V, achieves the optimal regret $\tilde{\mathcal{O}}(\sqrt{DSAT})$ up to logarithmic factors, and so our work closes a gap with the lower bound without additional assumptions on the MDP. We perform experiments in a variety of environments that validates the theoretical bounds as well as prove UCRL-V to be better than the state-of-the-art algorithms.

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BibTeX RIS

Aristide Tossou, Debabrota Basu, Christos Dimitrakakis. 2019-05-27. Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities. https://arxiv.org/abs/1905.12425

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