AI RESEARCH
Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernels
arXiv CS.LG
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ArXi:2506.18186v3 Announce Type: replace The restless multi-armed bandit (RMAB) framework is a popular approach to solving resource allocation problems in networked systems. In this paper, we study optimal resource allocation in RMABs facing unknown and non-stationary dynamics. Solving RMABs optimally is known to be PSPACE-hard even with full knowledge of model parameters. While Whittle index policies offer asymptotic optimality with low computational cost, they require access to stationary transition kernels, an unrealistic assumption in many modern networking applications.