Towards Q-learning the whittle index for restless bandits

Abstract

We consider the multi-armed restless bandit problem (RMABP) with an infinite horizon average cost objective. Each arm of the RMABP is associated with a Markov process that operates in two modes: active and passive. At each time slot a controller needs to designate a subset of the arms to be active, of which the associated processes will evolve differently from the passive case. Treated as an optimal control problem, the optimal solution of the RMABP is known to be computationally intractable. In many cases, the Whittle index policy achieves near optimal performance and can be tractably found. Nevertheless, computation of the Whittle indices requires knowledge of the transition matrices of th..