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Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects

Min Hua, Dong Chen, Xinda Qi, Kun Jiang, Zemin Eitan Liu, Quan Zhou, Hongming Xu

TL;DR

A comprehensive review of MARL’s application in CAV control, offering insights to help practitioners bridge the gap between research and deployment, facilitating the development of more scalable, adaptive, and reliable CAV control.

Abstract

Connected and automated vehicles (CAVs) are considered a potential solution for future transportation challenges, aiming to develop systems that are efficient, safe, and environmentally friendly. However, CAV control presents significant challenges due to the complexity of interconnectivity and coordination required among vehicles. Multi-agent reinforcement learning (MARL), which has shown notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, emerges as a promising tool to enhance CAV capabilities. Despite its potential, there is a notable absence of current reviews on mainstream MARL algorithms for CAVs. To fill this gap, this paper offers a comprehensive review of MARL's application in CAV control. The paper begins with an introduction to MARL, explaining its unique advantages in handling complex and multi-agent scenarios. It then presents a detailed survey of MARL applications across various control dimensions for CAVs, including critical scenarios such as platooning control, lane-changing, and unsignalized intersections. Additionally, the paper reviews prominent simulation platforms essential for developing and testing MARL algorithms. Lastly, it examines the current challenges in deploying MARL for CAV control, including macro-micro optimization, communication, mixed traffic, and sim-to-real challenges. Potential solutions discussed include hierarchical MARL, decentralized MARL, adaptive interactions, and offline MARL.

Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects

TL;DR

A comprehensive review of MARL’s application in CAV control, offering insights to help practitioners bridge the gap between research and deployment, facilitating the development of more scalable, adaptive, and reliable CAV control.

Abstract

Connected and automated vehicles (CAVs) are considered a potential solution for future transportation challenges, aiming to develop systems that are efficient, safe, and environmentally friendly. However, CAV control presents significant challenges due to the complexity of interconnectivity and coordination required among vehicles. Multi-agent reinforcement learning (MARL), which has shown notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, emerges as a promising tool to enhance CAV capabilities. Despite its potential, there is a notable absence of current reviews on mainstream MARL algorithms for CAVs. To fill this gap, this paper offers a comprehensive review of MARL's application in CAV control. The paper begins with an introduction to MARL, explaining its unique advantages in handling complex and multi-agent scenarios. It then presents a detailed survey of MARL applications across various control dimensions for CAVs, including critical scenarios such as platooning control, lane-changing, and unsignalized intersections. Additionally, the paper reviews prominent simulation platforms essential for developing and testing MARL algorithms. Lastly, it examines the current challenges in deploying MARL for CAV control, including macro-micro optimization, communication, mixed traffic, and sim-to-real challenges. Potential solutions discussed include hierarchical MARL, decentralized MARL, adaptive interactions, and offline MARL.
Paper Structure (29 sections, 12 equations, 10 figures, 4 tables)

This paper contains 29 sections, 12 equations, 10 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methodologies
  3. Preliminaries of RL
  4. Value-Based Methods
  5. Policy-Based Methods
  6. Actor-Critic Methods
  7. Preliminaries of MARL
  8. Training and execution strategies in MARL
  9. Centralized training with decentralized execution (CTDE)
  10. Decentralized training with decentralized execution (DTDE)
  11. MARL algorithm variants
  12. Value function decomposition
  13. Learning to Communicate
  14. Hierarchical Structure
  15. Causal Inference
  16. ...and 14 more sections

Figures (10)

  • Figure 1: The multi-agent control system for CAVs: the left side represents the information inputs of the overall control system, while the right side depicts the studied multi-agent control system for CAVs.
  • Figure 2: Illustration of reinforcement learning (RL).
  • Figure 3: Illustration of multi-agent reinforcement learning (MARL).
  • Figure 4: Illustration of centralized training with decentralized execution (CTDE) in MARL.
  • Figure 5: Illustration of decentralized training with decentralized execution (DTDE) in MARL.
  • ...and 5 more figures