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arXiv · 2004.14427

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

Konstantin E. Avrachenkov·Vivek S. Borkar
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Abstract

A novel reinforcement learning algorithm is introduced for multiarmed restless bandits with average reward, using the paradigms of Q-learning and Whittle index. Specifically, we leverage the structure of the Whittle index policy to reduce the search space of Q-learning, resulting in major computational gains. Rigorous convergence analysis is provided, supported by numerical experiments. The numerical experiments show excellent empirical performance of the proposed scheme.

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BibTeXRIS

Konstantin E. Avrachenkov, Vivek S. Borkar. 2020-04-29. Whittle index based Q-learning for restless bandits with average reward. https://doi.org/10.1016/j.automatica.2022.110186

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