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Found 8 papers, 2 papers with code
Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital Health
RMABs are increasingly being used for sensitive decisions such as in public health, treatment scheduling, anti-poaching, and -- the motivation for this work -- digital health.
Q-Learning Lagrange Policies for Multi-Action Restless Bandits
Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which $N$ independent processes are managed.
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications.
Collapsing Bandits and Their Application to Public Health Interventions
(ii) We exploit the optimality of threshold policies to build fast algorithms for computing the Whittle index, including a closed-form.
Envisioning Communities: A Participatory Approach Towards AI for Social Good
We argue that AI for social good ought to be assessed by the communities that the AI system will impact, using as a guide the capabilities approach, a framework to measure the ability of different policies to improve human welfare equity.
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning
Our approach uses a double oracle framework (oracles for \textit{agent} and \textit{nature}), which is often used for single-process robust planning but requires significant new techniques to accommodate the combinatorial nature of RMABs.
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks
The goal is to plan an intervention schedule that maximizes the expected reward while satisfying budget constraints on each worker as well as fairness in terms of the load assigned to each worker.
Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare
The success of many healthcare programs depends on participants' adherence.
