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Motivated by applications such as machine repair, project monitoring, andanti-poaching patrol scheduling, we study intervention planning of stochasticprocesses under resource constraints. This planning problem has previously beenmodeled as restless multi-armed bandits (RMAB), where each arm is anintervention-dependent Markov Decision Process. However, the existingliterature assumes all intervention resources belong to a single uniform pool,limiting their applicability to real-world settings where interventions arecarried out by a set of workers, each with their own costs, budgets, andintervention effects. In this work, we consider a novel RMAB setting, calledmulti-worker restless bandits (MWRMAB) with heterogeneous workers. The goal isto plan an intervention schedule that maximizes the expected reward whilesatisfying budget constraints on each worker as well as fairness in terms ofthe load assigned to each worker. Our contributions are two-fold: (1) weprovide a multi-worker extension of the Whittle index to tackle heterogeneouscosts and per-worker budget and (2) we develop an index-based scheduling policyto achieve fairness. Further, we evaluate our method on various cost structuresand show that our method significantly outperforms other baselines in terms offairness without sacrificing much in reward accumulated.

Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks