31 citations · 117 across the 19 of their papers we have counts for
4 papers · 1 filter
Learning Optimal Admission Control in Partially Observable Queueing Networks
Jonatha Anselmi, Bruno Gaujal, Louis-Sébastien Rebuffi
We present an efficient reinforcement learning algorithm that learns the optimal admission control policy in a partially observable queueing network. Specifically, only the arrival…
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space
Jonatha Anselmi, Bruno Gaujal, Louis-Sébastien Rebuffi
In this paper, we revisit the regret of undiscounted reinforcement learning in MDPs with a birth and death structure. Specifically, we consider a controlled queue with impatient jo…
Decentralized model-free reinforcement learning in stochastic games with average-reward objective
Romain Cravic, Nicolas Gast, Bruno Gaujal
We propose the first model-free algorithm that achieves low regret performance for decentralized learning in two-player zero-sum tabular stochastic games with infinite-horizon aver…
Reinforcement Learning for Markovian Bandits: Is Posterior Sampling more Scalable than Optimism?
Nicolas Gast, Bruno Gaujal, Kimang Khun
We study learning algorithms for the classical Markovian bandit problem with discount. We explain how to adapt PSRL [24] and UCRL2 [2] to exploit the problem structure. These varia…