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

## TL;DR

This paper investigates optimal scheduling policies for energy-harvesting sensors to minimize the age of information, using reinforcement learning in unknown environments and analyzing feedback mechanisms.

## Contribution

It introduces a reinforcement learning approach for AoI minimization in energy-harvesting sensors with unknown parameters, extending prior work with real-time learning capabilities.

## Key findings

- Reinforcement learning effectively minimizes AoI in unknown environments.
- Optimal policies depend on feedback mechanisms and system parameters.
- Numerical results validate the proposed methods' effectiveness.

## Abstract

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.