# Policy Gradient Algorithms for Age-of-Information Cost Minimization

arXiv: https://arxiv.org/abs/2512.11990 · HTML: https://mlsanity.com/paper/2512.11990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 10 of 10)

- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Sampling for Data Freshness Optimization: Non-linear Age Functions](https://mlsanity.com/paper/1812.07241/) (2018)
- [A Reinforcement Learning Framework for Optimizing Age-of-Information in RF-powered Communication Systems](https://mlsanity.com/paper/1908.06367/) (2019)
- [AoI Minimization in Status Update Control with Energy Harvesting Sensors](https://mlsanity.com/paper/2009.04224/) (2020)
- [A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ](https://mlsanity.com/paper/2102.09774/) (2021)
- [Timely Cloud Computing: Preemption and Waiting](https://mlsanity.com/paper/1907.05408/) (2019)
- [The Age of Information in Multihop Networks](https://mlsanity.com/paper/1712.10061/) (2017)
- [Average Age of Information Penalty of Short-Packet Communications with Packet Management](https://mlsanity.com/paper/2210.15672/) (2022)
- [Non-Linear Age of Information: An Energy Efficient Receiver-Centric Approach](https://mlsanity.com/paper/2112.15553/) (2021)
- [When to Preempt in a Status Update System?](https://mlsanity.com/paper/2402.00845/) (2024)

## Abstract (arXiv)

Recent developments in cyber-physical systems have increased the importance of maximizing the freshness of the information about the physical environment. However, optimizing the access policies of Internet of Things devices to maximize the data freshness, measured as a function of the Age-of-Information (AoI) metric, is a challenging task. This work introduces two algorithms to optimize the information update process in cyber-physical systems operating under the generate-at-will model, by finding an online policy without knowing the characteristics of the transmission delay or the age cost function. The optimization seeks to minimize the time-average cost, which integrates the AoI at the receiver and the data transmission cost, making the approach suitable for a broad range of scenarios. Both algorithms employ policy gradient methods within the framework of model-free reinforcement learning (RL) and are specifically designed to handle continuous state and action spaces. Each algorithm minimizes the cost using a distinct strategy for deciding when to send an information update. Moreover, we demonstrate that it is feasible to apply the two strategies simultaneously, leading to an additional reduction in cost. The results demonstrate that the proposed algorithms exhibit good convergence properties and achieve a time-average cost within 3% of the optimal value, when the latter is computable. A comparison with other state-of-the-art methods shows that the proposed algorithms outperform them in one or more of the following aspects: being applicable to a broader range of scenarios, achieving a lower time-average cost, and requiring a computational cost at least one order of magnitude lower.