mlsanityData to 7 Oct 2026

Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information

arXiv 2203.15781 ↗First posted 28 Mar 2022eess.SY cs.AI cs.SYcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 34 references that are in the map. First posted on arXiv 28 Mar 2022.

Builds on

9 of 34 references.

References · 9

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2019Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Drivingcited by 56
  3. 2019Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Controlcited by 19
  4. 2020Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgridscited by 13
  5. 2019Learning Compact Models for Planning with Exogenous Processescited by 11
  6. 2019Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learningcited by 10
  7. 2021Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Controlcited by 5
  8. 2018Benefits of V2V Communication for Autonomous and Connected Vehiclescited by 3
  9. 2016Safely Optimizing Highway Traffic with Robust Model Predictive Control-based Cooperative Adaptive Cruise Controlcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 0; 2025: 1.

5 papers.

  1. 2022Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programmingcited by 4
  2. 2023Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocationcited by 4
  3. 2023Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Controlcited by 2
  4. 2023Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Controlcited by 0
  5. 2025Learning Value of Information towards Joint Communication and Control in 6G V2Xcited by 0