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A novel reinforcement learning algorithm is introduced for multiarmedrestless bandits with average reward, using the paradigms of Q-learning andWhittle index. Specifically, we leverage the structure of the Whittle indexpolicy to reduce the search space of Q-learning, resulting in majorcomputational gains. Rigorous convergence analysis is provided, supported bynumerical experiments. The numerical experiments show excellent empiricalperformance of the proposed scheme.

Whittle index based Q-learning for restless bandits with average reward