Abstract

In this paper, we study the scalability of model-based algorithms learning the optimal policy of a discounted \blue{rested} Markovian bandit problem with $n$ arms. There are two categories of model-based reinforcement learning algorithms: Bayesian algorithms (like PSRL), and optimistic algorithms (like UCRL2 or UCBVI). A naive application of these algorithms is not scalable because the state-space is exponential in $n$. In this paper, we construct variants of these algorithms specially tailored to Markovian bandits (MB) that we call MB-PSRL, MB-UCRL2, and MB-UCBVI. \blue{We consider an episodic setting with geometrically distributed episode length, and measure the performance of the algorithm in terms of regret (Bayesian regret for MB-PSRL and expected regret for MB-UCRL2 and MB-UCBVI)}. We prove that, for this setting, all algorithms have a low regret in $\tilde{O}(S\sqrt{nK})$ -- where $K$ is the number of episodes, $n$ is the number of arms and $S$ is the number of states of each arm. Up to a factor $\sqrt{S}$, these regrets match the \blue{Bayesian minimax regret} lower bound of $\Omega(\sqrt{SnK})$ that we also derive. Even if their theoretical regrets are comparable, the {\it time complexities} of these algorithms vary greatly: We show that MB-UCRL2, as well as all algorithms that use bonuses on transition matrices have a { time} complexity that grows exponentially in $n$. In contrast, MB-UCBVI does not use bonuses on transition matrices and we show that it can be implemented efficiently, with a time complexity linear in $n$. Our numerical experiments show, however, that its empirical regret is large. Our Bayesian algorithm, MB-PSRL, enjoys the best of both worlds: its running time is linear in the number of arms and its empirical regret is the smallest of all algorithms. This is a new addition in the understanding of the power of Bayesian algorithms, that can often be tailored to the structure of the problems to learn.