Proposes a near-optimal index policy for restless bandits with individual penalty constraints, advancing sequential decision-making theory. Provides both theoretical performance guarantees and practical learning algorithms for this class of optimization problems.
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Restless Bandits with Individual Penalty Constraints: A New Near-Optimal Index Policy and How to Learn It
Near-optimal index policy for restless bandits now handles individual penalty constraints, bridging theory-practice gap with guaranteed algorithms for constrained sequential decision-making.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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