31 citations · 117 across the 19 of their papers we have counts for
19 papers
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…
Reoptimization Nearly Solves Weakly Coupled Markov Decision Processes
Nicolas Gast, Bruno Gaujal, Chen Yan
We propose a new policy, called the LP-update policy, to solve finite horizon weakly-coupled Markov decision processes. The latter can be seen as multi-constraint multi-action band…
Testing Indexability and Computing Whittle and Gittins Index in Subcubic Time
Nicolas Gast, Bruno Gaujal, Kimang Khun
Whittle index is a generalization of Gittins index that provides very efficient allocation rules for restless multi-armed bandits. In this work, we develop an algorithm to test the…
LP-based policies for restless bandits: necessary and sufficient conditions for (exponentially fast) asymptotic optimality
Nicolas Gast, Bruno Gaujal, Chen Yan
We provide a framework to analyse control policies for the restless Markovian bandit model, under both finite and infinite time horizon. We show that when the population of arms go…