Online Reinforcement Learning for Digital Health Interventions in the Dyadic Setting

Susan Murphy gives her talk, “Online Reinforcement Learning for Digital Health Interventions in the Dyadic Setting,” on September 23, 2026.

EECS Colloquium + Neyman + Climb

September 23, 2026

310 Sutardja Dai Hall (Banatao Auditorium)
4:00 – 5:00 pm

Susan Murphy

Mallinckrodt Professor of Statistics and of Computer Science and Associate Faculty at the Kempner Institute, Harvard University

Bio

Susan A. Murphy is Mallinckrodt Professor of Statistics and of Computer Science and Associate Faculty at the Kempner Institute, Harvard University. Her research focuses on improving sequential decision making via the development of online, real-time reinforcement learning algorithms. Her lab is involved in multiple deployments of these algorithms in digital health. She is a member of the US National Academy of Sciences and of the US National Academy of Medicine. In 2013 she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision making. She is a Fellow of the College on Problems in Drug Dependence, Past-President of Institute of Mathematical Statistics, Past-President of the Bernoulli Society and a former editor of the Annals of Statistics. 

Abstract

We present our ongoing work on the development of an online reinforcement learning (RL) algorithm that personalizes delivery of interventions sequentially, over time, to members of a dyad. The RL algorithm is one component in the design of the digital health intervention. Different RL components target different elements of the dyad; these elements are a target individual, a carepartner and their relationship. The RL algorithm is a multi-agent RL algorithm in which the 3 agents make decisions on the 3 elements of the dyad. We incorporate domain knowledge in the form of approximal causal directed acyclic graphs to speed up online learning in this sparse data setting. This work is motivated by our development of the ADAPTS-HCT multi-agent RL algorithm, designed to improve medication adherence by young adults who have undergone a blood and bone marrow transplant. The RL algorithm will be deployed in a clinical trial in late summer 2026.