# Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost

arXiv: https://arxiv.org/abs/1902.09467 · HTML: https://mlsanity.com/paper/1902.09467/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

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

- [A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ](https://mlsanity.com/paper/2102.09774/) (2021)
- [DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs](https://mlsanity.com/paper/2209.08646/) (2022)
- [Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting](https://mlsanity.com/paper/2106.16037/) (2021)
- [Age Optimal Sampling for Unreliable Channels under Unknown Channel Statistics](https://mlsanity.com/paper/2412.18119/) (2024)
- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)

## Abstract (arXiv)

The time average expected age of information (AoI) is studied for status updates sent from an energy-harvesting transmitter with a finite-capacity battery. The optimal scheduling policy is first studied under different feedback mechanisms when the channel and energy harvesting statistics are known. For the case of unknown environments, an average-cost reinforcement learning algorithm is proposed that learns the system parameters and the status update policy in real time. The effectiveness of the proposed methods is verified through numerical results.