# A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning

arXiv: https://arxiv.org/abs/2512.21412 · HTML: https://mlsanity.com/paper/2512.21412/

## 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 73 of its 185 references that are in the map.
- First posted on arXiv 24 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, 30 of 73)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Self-Supervised Exploration via Disagreement](https://mlsanity.com/paper/1906.04161/) (2019)
- [Reinforcement Learning for Combinatorial Optimization: A Survey](https://mlsanity.com/paper/2003.03600/) (2020)
- [Learning with Opponent-Learning Awareness](https://mlsanity.com/paper/1709.04326/) (2017)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Reinforcement Learning with Random Delays](https://mlsanity.com/paper/2010.02966/) (2020)
- [A Survey for Solving Mixed Integer Programming via Machine Learning](https://mlsanity.com/paper/2203.02878/) (2022)
- [Real-Time Reinforcement Learning](https://mlsanity.com/paper/1911.04448/) (2019)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019)
- [Spatial Deep Learning for Wireless Scheduling](https://mlsanity.com/paper/1808.01486/) (2018)
- [Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks](https://mlsanity.com/paper/1801.01803/) (2018)
- [A Linearly Convergent Conditional Gradient Algorithm with Applications to Online and Stochastic Optimization](https://mlsanity.com/paper/1301.4666/) (2013)
- [Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/2007.14297/) (2020)
- [AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2105.04196/) (2021)
- [Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases](https://mlsanity.com/paper/2203.07345/) (2022)
- [Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-assisted Networks](https://mlsanity.com/paper/1905.02993/) (2019)
- [Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks](https://mlsanity.com/paper/2406.11245/) (2024)
- [On the Role of Age of Information in the Internet of Things](https://mlsanity.com/paper/1812.08286/) (2018)
- [On The Age Of Information In Status Update Systems With Packet Management](https://mlsanity.com/paper/1506.08637/) (2015)

## Abstract (arXiv)

The age of information (AoI) has become a central measure of data freshness in modern wireless systems, yet existing surveys either focus on classical AoI formulations or provide broad discussions of reinforcement learning (RL) in wireless networks without addressing freshness as a unified learning problem. Motivated by this gap, this survey examines RL specifically through the lens of AoI and generalized freshness optimization. We organize AoI and its variants into native, function-based, and application-oriented families, providing a clearer view of how freshness should be modeled in B5G and 6G systems. Building on this foundation, we introduce a policy-centric taxonomy that reflects the decisions most relevant to freshness, consisting of update-control RL, medium-access RL, risk-sensitive RL, and multi-agent RL. This structure provides a coherent framework for understanding how learning can support sampling, scheduling, trajectory planning, medium access, and distributed coordination. We further synthesize recent progress in RL-driven freshness control and highlight open challenges related to delayed decision processes, stochastic variability, and cross-layer design. The goal is to establish a unified foundation for learning-based freshness optimization in next-generation wireless networks.