mlsanityData to 7 Oct 2026

Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming

arXiv 2206.07536 ↗First posted 15 Jun 2022eess.SY cs.AI cs.LGcited by 4

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Abstract (arXiv)

Deep Reinforcement Learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle. However, it is more challenging to learn a stable and efficient car-following policy when there are multiple following vehicles in a platoon, especially with unpredictable leading vehicle behavior. In this context, we adopt an integrated DRL and Dynamic Programming (DP) approach to learn autonomous platoon control policies, which embeds the Deep Deterministic Policy Gradient (DDPG) algorithm into a finite-horizon value iteration framework. Although the DP framework can improve the stability and performance of DDPG, it has the limitations of lower sampling and training efficiency. In this paper, we propose an algorithm, namely Finite-Horizon-DDPG with Sweeping through reduced state space using Stationary approximation (FH-DDPG-SS), which uses three key ideas to overcome the above limitations, i.e., transferring network weights backward in time, stationary policy approximation for earlier time steps, and sweeping through reduced state space. In order to verify the effectiveness of FH-DDPG-SS, simulation using real driving data is performed, where the performance of FH-DDPG-SS is compared with those of the benchmark algorithms. Finally, platoon safety and string stability for FH-DDPG-SS are demonstrated.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 53 references that are in the map. First posted on arXiv 15 Jun 2022.

Builds on

17 of 53 references.

References · 17

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  4. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  5. 2015Learning Continuous Control Policies by Stochastic Value Gradientscited by 156
  6. 2017The Uncertainty Bellman Equation and Explorationcited by 81
  7. 2019Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Drivingcited by 56
  8. 2019Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challengescited by 36
  9. 2019Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Controlcited by 19
  10. 2020Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgridscited by 13
  11. 2019Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learningcited by 10
  12. 2016Distributed Model Predictive Control for Heterogeneous Vehicle Platoons under Unidirectional Topologiescited by 6
5 more
  1. 2021Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Controlcited by 5
  2. 2022Deep Reinforcement Learning Aided Platoon Control Relying on V2X Informationcited by 5
  3. 2021A Secure Sensor Fusion Framework for Connected and Automated Vehicles under Sensor Attackscited by 4
  4. 2016Safely Optimizing Highway Traffic with Robust Model Predictive Control-based Cooperative Adaptive Cruise Controlcited by 2
  5. 2018Ethical Implications of Social Internet of Vehicles Systemscited by 2

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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocationcited by 4
  2. 2023Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Controlcited by 2
  3. 2024Dyna-Style Learning with A Macroscopic Model for Vehicle Platooning in Mixed-Autonomy Trafficcited by 0
  4. 2025Learning Value of Information towards Joint Communication and Control in 6G V2Xcited by 0