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  1. ; ; ; (, Mathematics of Operations Research)
    We consider the infinite-horizon, average-reward restless bandit problem in discrete time. We propose a new class of policies that are designed to drive a progressively larger subset of arms toward the optimal distribution. We show that our policies are asymptotically optimal with an [Formula: see text] optimality gap for an N-armed problem, assuming only a unichain and aperiodicity assumption. Our approach departs from most existing work that focuses on index or priority policies, which rely on the Global Attractor Property to guarantee convergence to the optimum, or a recently developed simulation-based policy, which requires a Synchronization Assumption. 
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  2. ; ; (, Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  3. ; (, Neural Information Processing Systems (NeurIPS) 2025)
    Accepted Manuscript Full Text Available
  4. ; (, Conference on Learning Theory (COLT), 2025.)
    Accepted Manuscript Full Text Available
  5. ; ; (, International Conference on Machine Learning (ICML))
    Accepted Manuscript Full Text Available
  6. ; ; ; (, Neural Information Processing Systems (NeurIPS), 2025)
    Accepted Manuscript Full Text Available
  7. ; ; ; (, International Conference on Machine Learning (ICML),)
    Accepted Manuscript Full Text Available
  8. ; ; ; (, International Conference on Artificial Intelligence and Statistics (AISTATS), 2025)
    Accepted Manuscript Full Text Available
  9. ; ; (, Innovations in Theoretical Computer Science (ITCS), 2025.)
    Accepted Manuscript Full Text Available
  10. ; ; ; (, ACM SIGMETRICS 2025.)
    Accepted Manuscript Full Text Available