# Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information

arXiv: https://arxiv.org/abs/2203.15781 · HTML: https://mlsanity.com/paper/2203.15781/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1902.00089/) (2019)
- [Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control](https://mlsanity.com/paper/1910.12047/) (2019)
- [Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids](https://mlsanity.com/paper/2002.02581/) (2020)
- [Learning Compact Models for Planning with Exogenous Processes](https://mlsanity.com/paper/1909.13870/) (2019)
- [Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning](https://mlsanity.com/paper/1905.08314/) (2019)
- [Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control](https://mlsanity.com/paper/2103.03796/) (2021)
- [Benefits of V2V Communication for Autonomous and Connected Vehicles](https://mlsanity.com/paper/1803.02900/) (2018)
- [Safely Optimizing Highway Traffic with Robust Model Predictive Control-based Cooperative Adaptive Cruise Control](https://mlsanity.com/paper/1605.07493/) (2016)

## Built on by (most cited first, 5 of 5)

- [Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming](https://mlsanity.com/paper/2206.07536/) (2022)
- [Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation](https://mlsanity.com/paper/2311.11280/) (2023)
- [Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control](https://mlsanity.com/paper/2311.11281/) (2023)
- [Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control](https://mlsanity.com/paper/2310.10948/) (2023)
- [Learning Value of Information towards Joint Communication and Control in 6G V2X](https://mlsanity.com/paper/2505.06978/) (2025)

## Abstract (arXiv)

The impact of Vehicle-to-Everything (V2X) communications on platoon control performance is investigated. Platoon control is essentially a sequential stochastic decision problem (SSDP), which can be solved by Deep Reinforcement Learning (DRL) to deal with both the control constraints and uncertainty in the platoon leading vehicle's behavior. In this context, the value of V2X communications for DRL-based platoon controllers is studied with an emphasis on the tradeoff between the gain of including exogenous information in the system state for reducing uncertainty and the performance erosion due to the curse-of-dimensionality. Our objective is to find the specific set of information that should be shared among the vehicles for the construction of the most appropriate state space. SSDP models are conceived for platoon control under different information topologies (IFT) by taking into account `just sufficient' information. Furthermore, theorems are established for comparing the performance of their optimal policies. In order to determine whether a piece of information should or should not be transmitted for improving the DRL-based control policy, we quantify its value by deriving the conditional KL divergence of the transition models. More meritorious information is given higher priority in transmission, since including it in the state space has a higher probability in offsetting the negative effect of having higher state dimensions. Finally, simulation results are provided to illustrate the theoretical analysis.