# Dyadic Reinforcement Learning

arXiv: https://arxiv.org/abs/2308.07843 · HTML: https://mlsanity.com/paper/2308.07843/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 74 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 30 of 30)

- [Minimax Regret Bounds for Reinforcement Learning](https://mlsanity.com/paper/1703.05449/) (2017)
- [Data-Efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1805.08296/) (2018)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [FeUdal Networks for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1703.01161/) (2017)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Contextual Markov Decision Processes](https://mlsanity.com/paper/1502.02259/) (2015)
- [Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning](https://mlsanity.com/paper/1703.07710/) (2017)
- [Stochastic Neural Networks for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1704.03012/) (2017)
- [Generalization and Exploration via Randomized Value Functions](https://mlsanity.com/paper/1402.0635/) (2014)
- [A Laplacian Framework for Option Discovery in Reinforcement Learning](https://mlsanity.com/paper/1703.00956/) (2017)
- [Frequentist Regret Bounds for Randomized Least-Squares Value Iteration](https://mlsanity.com/paper/1911.00567/) (2019)
- [Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning](https://mlsanity.com/paper/1703.02949/) (2017)
- [Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity](https://mlsanity.com/paper/1909.03539/) (2019)
- [Worst-Case Regret Bounds for Exploration via Randomized Value Functions](https://mlsanity.com/paper/1906.02870/) (2019)
- [Contextualize Me -- The Case for Context in Reinforcement Learning](https://mlsanity.com/paper/2202.04500/) (2022)
- [Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies](https://mlsanity.com/paper/1807.07665/) (2018)
- [Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards](https://mlsanity.com/paper/1910.04450/) (2019)
- [A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients](https://mlsanity.com/paper/1605.04070/) (2016)
- [Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines](https://mlsanity.com/paper/2206.03944/) (2022)
- [CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning](https://mlsanity.com/paper/2110.02102/) (2021)
- [Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling](https://mlsanity.com/paper/1907.11625/) (2019)
- [Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration](https://mlsanity.com/paper/2010.12163/) (2020)
- [Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care](https://mlsanity.com/paper/2208.07406/) (2022)
- [Contingency-Aware Influence Maximization: A Reinforcement Learning Approach](https://mlsanity.com/paper/2106.07039/) (2021)
- [Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty - An Extended Version](https://mlsanity.com/paper/1602.00165/) (2016)
- [CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks](https://mlsanity.com/paper/2107.03603/) (2021)
- [Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes](https://mlsanity.com/paper/2106.15380/) (2021)
- [Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling](https://mlsanity.com/paper/2304.05365/) (2023)
- [Rapidly Personalizing Mobile Health Treatment Policies with Limited Data](https://mlsanity.com/paper/2002.09971/) (2020)
- [Influence Maximization for Social Good: Use of Social Networks in Low Resource Communities](https://mlsanity.com/paper/1912.02105/) (2019)

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning on Dyads to Enhance Medication Adherence](https://mlsanity.com/paper/2502.06835/) (2025)

## Abstract (arXiv)

Mobile health aims to enhance health outcomes by delivering interventions to individuals as they go about their daily life. The involvement of care partners and social support networks often proves crucial in helping individuals managing burdensome medical conditions. This presents opportunities in mobile health to design interventions that target the dyadic relationship -- the relationship between a target person and their care partner -- with the aim of enhancing social support. In this paper, we develop dyadic RL, an online reinforcement learning algorithm designed to personalize intervention delivery based on contextual factors and past responses of a target person and their care partner. Here, multiple sets of interventions impact the dyad across multiple time intervals. The developed dyadic RL is Bayesian and hierarchical. We formally introduce the problem setup, develop dyadic RL and establish a regret bound. We demonstrate dyadic RL's empirical performance through simulation studies on both toy scenarios and on a realistic test bed constructed from data collected in a mobile health study.