# Learning Value of Information towards Joint Communication and Control in 6G V2X

arXiv: https://arxiv.org/abs/2505.06978 · HTML: https://mlsanity.com/paper/2505.06978/

## 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 19 of its 45 references that are in the map.
- First posted on arXiv 11 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, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019)
- [Learning Agent Communication under Limited Bandwidth by Message Pruning](https://mlsanity.com/paper/1912.05304/) (2019)
- [Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks](https://mlsanity.com/paper/2010.01722/) (2020)
- [Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks](https://mlsanity.com/paper/2309.06021/) (2023)
- [Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information](https://mlsanity.com/paper/2203.15781/) (2022)
- [Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming](https://mlsanity.com/paper/2206.07536/) (2022)
- [Investigating Value of Information in Future Vehicular Communications](https://mlsanity.com/paper/1907.10124/) (2019)
- [Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation](https://mlsanity.com/paper/2311.11280/) (2023)
- [Towards the Age of Intelligent Vehicular Networks for Connected and Autonomous Vehicles in 6G](https://mlsanity.com/paper/2209.01349/) (2022)
- [Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control](https://mlsanity.com/paper/2311.11281/) (2023)
- [Fairness for Freshness: Optimal Age of Information Based OFDMA Scheduling with Minimal Knowledge](https://mlsanity.com/paper/2010.07139/) (2020)
- [Digital Retina for IoV Towards 6G: Architecture, Opportunities, and Challenges](https://mlsanity.com/paper/2401.04916/) (2024)

## Abstract (arXiv)

As Cellular Vehicle-to-Everything (C-V2X) evolves towards future sixth-generation (6G) networks, Connected Autonomous Vehicles (CAVs) are emerging to become a key application. Leveraging data-driven Machine Learning (ML), especially Deep Reinforcement Learning (DRL), is expected to significantly enhance CAV decision-making in both vehicle control and V2X communication under uncertainty. These two decision-making processes are closely intertwined, with the value of information (VoI) acting as a crucial bridge between them. In this paper, we introduce Sequential Stochastic Decision Process (SSDP) models to define and assess VoI, demonstrating their application in optimizing communication systems for CAVs. Specifically, we formally define the SSDP model and demonstrate that the MDP model is a special case of it. The SSDP model offers a key advantage by explicitly representing the set of information that can enhance decision-making when available. Furthermore, as current research on VoI remains fragmented, we propose a systematic VoI modeling framework grounded in the MDP, Reinforcement Learning (RL) and Optimal Control theories. We define different categories of VoI and discuss their corresponding estimation methods. Finally, we present a structured approach to leverage the various VoI metrics for optimizing the ``When", ``What", and ``How" to communicate problems. For this purpose, SSDP models are formulated with VoI-associated reward functions derived from VoI-based optimization objectives. While we use a simple vehicle-following control problem to illustrate the proposed methodology, it holds significant potential to facilitate the joint optimization of stochastic, sequential control and communication decisions in a wide range of networked control systems.