# Enhancing Information Freshness: An AoI Optimized Markov Decision Process

arXiv: https://arxiv.org/abs/2409.02424 · HTML: https://mlsanity.com/paper/2409.02424/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 3 of 3)

- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Optimizing Information Freshness in Wireless Networks under General Interference Constraints](https://mlsanity.com/paper/1803.06467/) (2018)
- [Underwater Differential Game: Finite-Time Target Hunting Task with Communication Delay](https://mlsanity.com/paper/2202.00329/) (2022)

## Abstract (arXiv)

Ocean exploration utilizing autonomous underwater vehicles (AUVs) via reinforcement learning (RL) has emerged as a significant research focus. However, underwater tasks have mostly failed due to the observation delay caused by information limitation in the information updating networks. In this study, we present an AoI optimized Markov decision process (AoI-MDP) to improve the performance of underwater tasks. Specifically, AoI-MDP models observation delay as timing delay through statistical delay formulation, and includes this delay as a new component in the state space. Additionally, we introduce wait time in the action space, and integrate AoI with reward functions to achieve joint optimization of information freshness and decision-making for AUVs leveraging RL for training. Finally, we apply this approach to the multi-AUV data collection task scenario as an example. Simulation results highlight the feasibility of AoI-MDP, which effectively minimizes AoI while showcasing superior performance in the task. To accelerate relevant research in this field, we have made the simulation codes available as open-source.