@conference {1664819,
	title = {Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks},
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
	year = {2023},
	abstract = {
	Motivated by applications such as machine repair, project monitoring,\&nbsp;and anti-poaching patrol scheduling, we study intervention\&nbsp;planning of stochastic processes under resource constraints.\&nbsp;This planning problem has previously been modeled as restless\&nbsp;multi-armed bandits (RMAB), where each arm is an interventiondependent\&nbsp;Markov Decision Process. However, the existing literature\&nbsp;assumes all intervention resources belong to a single uniform\&nbsp;pool, limiting their applicability to real-world settings where interventions\&nbsp;are carried out by a set of workers, each with their\&nbsp;own costs, budgets, and intervention effects. In this work, we consider\&nbsp;a novel RMAB setting, called multi-worker restless bandits\&nbsp;(MWRMAB) with heterogeneous workers. The goal is to plan an\&nbsp;intervention schedule that maximizes the expected reward while\&nbsp;satisfying budget constraints on each worker as well as fairness in\&nbsp;terms of the load assigned to each worker. Our contributions are\&nbsp;two-fold: (1) we provide a multi-worker extension of the Whittle\&nbsp;index to tackle heterogeneous costs and per-worker budget and\&nbsp;(2) we develop an index-based scheduling policy to achieve fairness.\&nbsp;Further, we evaluate our method on various cost structures and\&nbsp;show that our method significantly outperforms other baselines in\&nbsp;terms of fairness without sacrificing much in reward accumulated.

},
	author = {Arpita Biswas and Jackson A. Killian and Paula Rodriguez Diaz and Susobhan Ghosh and Tambe, Milind}
}
