The paper reformulates restless bandits as budgeted thresholding contextual bandits, proves non-asymptotic optimality for an oracle policy in a simplified setting, and shows a practical policy with sublinear regret and faster convergence than prior methods in heterogeneous environments.
Scalable decision-focused learning in restless multi-armed bandits with application to maternal and child health
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From Restless to Contextual: A Thresholding Bandit Reformulation For Finite-horizon Improvement
The paper reformulates restless bandits as budgeted thresholding contextual bandits, proves non-asymptotic optimality for an oracle policy in a simplified setting, and shows a practical policy with sublinear regret and faster convergence than prior methods in heterogeneous environments.