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Restless Bandits with Individual Penalty Constraints: Near-Optimal Indices and Deep Reinforcement Learning
This paper investigates the Restless Multi-Armed Bandit (RMAB) framework under individual penalty constraints to address resource allocation challenges in dynamic wireless networked environments. Unlike conventional RMAB models, our model allows each user (arm) to have distinct and stringent perform
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