# Networked Restless Bandits with Positive Externalities

arXiv: https://arxiv.org/abs/2212.05144 · HTML: https://mlsanity.com/paper/2212.05144/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case](https://mlsanity.com/paper/1910.07421/) (2019)
- [Collapsing Bandits and Their Application to Public Health Interventions](https://mlsanity.com/paper/2007.04432/) (2020)
- [Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems](https://mlsanity.com/paper/1905.12673/) (2019)
- [Thompson Sampling in Non-Episodic Restless Bandits](https://mlsanity.com/paper/1910.05654/) (2019)
- [Networked Restless Multi-Armed Bandits for Mobile Interventions](https://mlsanity.com/paper/2201.12408/) (2022)

## Built on by (most cited first, 5 of 5)

- [Global Rewards in Restless Multi-Armed Bandits](https://mlsanity.com/paper/2406.00738/) (2024)
- [EduQate: Generating Adaptive Curricula through RMABs in Education Settings](https://mlsanity.com/paper/2406.14122/) (2024)
- [Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting](https://mlsanity.com/paper/2409.05980/) (2024)
- [Networked Restless Multi-Arm Bandits with Reinforcement Learning](https://mlsanity.com/paper/2512.06274/) (2025)
- [Dynamic Treatment on Networks](https://mlsanity.com/paper/2605.06564/) (2026)

## Abstract (arXiv)

Restless multi-armed bandits are often used to model budget-constrained resource allocation tasks where receipt of the resource is associated with an increased probability of a favorable state transition. Prior work assumes that individual arms only benefit if they receive the resource directly. However, many allocation tasks occur within communities and can be characterized by positive externalities that allow arms to derive partial benefit when their neighbor(s) receive the resource. We thus introduce networked restless bandits, a novel multi-armed bandit setting in which arms are both restless and embedded within a directed graph. We then present Greta, a graph-aware, Whittle index-based heuristic algorithm that can be used to efficiently construct a constrained reward-maximizing action vector at each timestep. Our empirical results demonstrate that Greta outperforms comparison policies across a range of hyperparameter values and graph topologies.