# Efficient Information Updates in Compute-First Networking via Reinforcement Learning with Joint AoI and VoI

arXiv: https://arxiv.org/abs/2505.06025 · HTML: https://mlsanity.com/paper/2505.06025/

## 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 13 of its 29 references that are in the map.
- First posted on arXiv 9 May 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, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Age-of-Information vs. Value-of-Information Scheduling for Cellular Networked Control Systems](https://mlsanity.com/paper/1903.05356/) (2019)
- [Timely Communications for Remote Inference](https://mlsanity.com/paper/2404.16281/) (2024)
- [Query Age of Information: Freshness in Pull-Based Communication](https://mlsanity.com/paper/2105.06845/) (2021)
- [Goal-oriented Semantic Communications for Robotic Waypoint Transmission: The Value and Age of Information Approach](https://mlsanity.com/paper/2312.13182/) (2023)
- [Goal-Oriented Scheduling in Sensor Networks with Application Timing Awareness](https://mlsanity.com/paper/2306.03750/) (2023)
- [Age Based Task Scheduling and Computation Offloading in Mobile-Edge Computing Systems](https://mlsanity.com/paper/1905.11570/) (2019)
- [Age of Processing: Age-driven Status Sampling and Processing Offloading for Edge Computing-enabled Real-time IoT Applications](https://mlsanity.com/paper/2003.10916/) (2020)
- [On Age and Value of Information in Status Update Systems](https://mlsanity.com/paper/2003.14069/) (2020)
- [Age of View: A New Metric for Evaluating Heterogeneous Information Fusion in Vehicular Cyber-Physical Systems](https://mlsanity.com/paper/2208.00358/) (2022)
- [Optimal Push and Pull-Based Edge Caching For Dynamic Content](https://mlsanity.com/paper/2401.03613/) (2024)
- [TPAoI: Ensuring Fresh Service Status at the Network Edge in Compute-First Networking](https://mlsanity.com/paper/2412.18391/) (2024)

## Abstract (arXiv)

Timely and efficient dissemination of service information is critical in compute-first networking systems, where user requests arrive dynamically and computing resources are constrained. In such systems, the access point (AP) plays a key role in forwarding user requests to a server based on its latest received service information. This paper considers a single-source, single-destination system and introduces an Age-and-Value-Aware (AVA) metric that jointly captures both the timeliness and the task relevance of service information. Unlike traditional freshness-based metrics, AVA explicitly incorporates variations in server-side service capacity and AP forwarding decisions, allowing more context-aware update evaluation. Building upon AVA, we propose a reinforcement learning-based update policy that learns to selectively transmit service information updates to the AP. It aims to maximize overall task success while minimizing unnecessary communications. Extensive simulations under diverse user request patterns and varying service capacities demonstrate that AVA reduces the update frequency by over 90% on average compared to baselines, with reductions reaching 98% in certain configurations. Crucially, this reduction is achieved without compromising the accuracy of task execution or the quality of decision making.