Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health

This paper studies restless multi-armed bandit (RMAB) problems with unknown arm transition dynamics but with known correlated arm features. The goal is to learn a model to predict transition dynamics given features, where the Whittle index policy solves the RMAB problems using predicted transitions. However, prior works often learn the model by maximizing the predictive accuracy instead of final RMAB solution quality, causing a mismatch between training and evaluation objectives. To address this shortcoming, we propose a novel approach for decision-focused learning in RMAB that directly trains the predictive model to maximize the Whittle index solution quality. We present three key contributions: (i) we establish differentiability of the Whittle index policy to support decision-focused learning; (ii) we significantly improve the scalability of decision-focused learning approaches in sequential problems, specifically RMAB problems; (iii) we apply our algorithm to a previously collected dataset of maternal and child health to demonstrate its performance. Indeed, our algorithm is the first for decision-focused learning in RMAB that scales to real-world problem sizes.

Authors

• Kai Wang
• Shresth Verma Harvard University
• Aditya Mate Harvard University
• Sanket Shah Harvard University
• Aparna Taneja Google Research
• Neha Madhiwalla ARMMAN
• Aparna Hegde ARMMAN
• Milind Tambe Harvard University Google Research

Keywords

• PEAI: Societal Impact Of AI
• PRS: Planning under Uncertainty
• PRS: Planning with Markov Models (MDPs, POMDPs)
• PRS: Scheduling under Uncertainty