# Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming

arXiv: https://arxiv.org/abs/2206.07536 · HTML: https://mlsanity.com/paper/2206.07536/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 17 of 17)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Learning Continuous Control Policies by Stochastic Value Gradients](https://mlsanity.com/paper/1510.09142/) (2015)
- [The Uncertainty Bellman Equation and Exploration](https://mlsanity.com/paper/1709.05380/) (2017)
- [Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1902.00089/) (2019)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (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)
- [Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning](https://mlsanity.com/paper/1905.08314/) (2019)
- [Distributed Model Predictive Control for Heterogeneous Vehicle Platoons under Unidirectional Topologies](https://mlsanity.com/paper/1603.03225/) (2016)
- [Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control](https://mlsanity.com/paper/2103.03796/) (2021)
- [Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information](https://mlsanity.com/paper/2203.15781/) (2022)
- [A Secure Sensor Fusion Framework for Connected and Automated Vehicles under Sensor Attacks](https://mlsanity.com/paper/2103.00883/) (2021)
- [Safely Optimizing Highway Traffic with Robust Model Predictive Control-based Cooperative Adaptive Cruise Control](https://mlsanity.com/paper/1605.07493/) (2016)
- [Ethical Implications of Social Internet of Vehicles Systems](https://mlsanity.com/paper/1806.09081/) (2018)

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

- [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)
- [Unveiling Uncertainty-Aware Autonomous Cooperative Learning Based Planning Strategy](https://mlsanity.com/paper/2510.11041/) (2025)
- [Dyna-Style Learning with A Macroscopic Model for Vehicle Platooning in Mixed-Autonomy Traffic](https://mlsanity.com/paper/2405.02062/) (2024)
- [Learning Value of Information towards Joint Communication and Control in 6G V2X](https://mlsanity.com/paper/2505.06978/) (2025)

## 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.