Policy Gradient Algorithms for Age-of-Information Cost Minimization

TL;DR

This paper introduces two novel policy gradient algorithms for optimizing information update policies in cyber-physical systems to minimize a combined Age-of-Information and transmission cost, without prior knowledge of system parameters.

Contribution

The work develops model-free reinforcement learning algorithms that handle continuous spaces for AoI cost minimization, demonstrating their effectiveness and broad applicability.

Findings

Algorithms converge well and achieve within 3% of optimal cost.

Outperform existing methods in cost reduction and scenario applicability.

Require significantly less computational resources than state-of-the-art approaches.

Abstract

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.