Minimax Regret Bounds for Reinforcement Learning


Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:263-272, 2017.


We consider the problem of provably optimal exploration in reinforcement learning for finite horizon MDPs. We show that an optimistic modification to value iteration achieves a regret bound of $\tilde {O}( \sqrt{HSAT} + H^2S^2A+H\sqrt{T})$ where $H$ is the time horizon, $S$ the number of states, $A$ the number of actions and $T$ the number of time-steps. This result improves over the best previous known bound $\tilde {O}(HS \sqrt{AT})$ achieved by the UCRL2 algorithm. The key significance of our new results is that when $T\geq H^3S^3A$ and $SA\geq H$, it leads to a regret of $\tilde{O}(\sqrt{HSAT})$ that matches the established lower bound of $\Omega(\sqrt{HSAT})$ up to a logarithmic factor. Our analysis contain two key insights. We use careful application of concentration inequalities to the optimal value function as a whole, rather than to the transitions probabilities (to improve scaling in $S$), and we define Bernstein-based “exploration bonuses” that use the empirical variance of the estimated values at the next states (to improve scaling in $H$).

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