# Age of Information: Whittle Index for Scheduling Stochastic Arrivals

arXiv: https://arxiv.org/abs/1801.03422 · HTML: https://mlsanity.com/paper/1801.03422/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2018.

## Built on by (most cited first, 21 of 21)

- [Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare](https://mlsanity.com/paper/2105.07965/) (2021)
- [Optimistic Whittle Index Policy: Online Learning for Restless Bandits](https://mlsanity.com/paper/2205.15372/) (2022)
- [Networked Restless Multi-Armed Bandits for Mobile Interventions](https://mlsanity.com/paper/2201.12408/) (2022)
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- [Timely Communications for Remote Inference](https://mlsanity.com/paper/2404.16281/) (2024)
- [Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare](https://mlsanity.com/paper/2305.12640/) (2023)
- [Lagrangian Index Policy for Restless Bandits with Average Reward](https://mlsanity.com/paper/2412.12641/) (2024)
- [Tabular and Deep Learning for the Whittle Index](https://mlsanity.com/paper/2406.02057/) (2024)
- [Improving Health Information Access in the World's Largest Maternal Mobile Health Program via Bandit Algorithms](https://mlsanity.com/paper/2407.12131/) (2024)
- [MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks](https://mlsanity.com/paper/2608.01128/) (2026)
- [Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach](https://mlsanity.com/paper/2307.01366/) (2023)
- [Age-of-information minimization under energy harvesting and non-stationary environment](https://mlsanity.com/paper/2504.04916/) (2025)
- [On the Regularity and Fairness of Combinatorial Multi-Armed Bandit](https://mlsanity.com/paper/2509.12457/) (2025)
- [Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics](https://mlsanity.com/paper/2509.18654/) (2025)
- [MARBLE: Multi-Armed Restless Bandits in Latent Markovian Environment](https://mlsanity.com/paper/2511.09324/) (2025)
- [Adaptive Requesting in Decentralized Edge Networks via Non-Stationary Bandits](https://mlsanity.com/paper/2601.08760/) (2026)
- [Restless Bandits with Individual Penalty Constraints: Near-Optimal Indices and Deep Reinforcement Learning](https://mlsanity.com/paper/2604.04101/) (2026)
- [A Modularized Framework for Piecewise-Stationary Restless Bandits](https://mlsanity.com/paper/2604.10177/) (2026)
- [Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning](https://mlsanity.com/paper/2607.27626/) (2026)
- [Threshold Structure of Optimal Policies in Restart POMDPs](https://mlsanity.com/paper/2608.10936/) (2026)

## Abstract (arXiv)

Age of information is a new concept that characterizes the freshness of information at end devices. This paper studies the age of information from a scheduling perspective. We consider a wireless broadcast network where a base-station updates many users on stochastic information arrivals. Suppose that only one user can be updated for each time. In this context, we aim at developing a transmission scheduling algorithm for minimizing the long-run average age. To develop a low-complexity transmission scheduling algorithm, we apply the Whittle's framework for restless bandits. We successfully derive the Whittle index in a closed form and establish the indexability. Based on the Whittle index, we propose a scheduling algorithm, while experimentally showing that it closely approximates an age-optimal scheduling algorithm.