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A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles.

Pamul Yadav1, Ashutosh Mishra1, Shiho Kim1

  • 1School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.

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This summary is machine-generated.

This survey explores Multi-Agent Reinforcement Learning (MARL) for Connected and Automated Vehicles (CAVs). It identifies current challenges and future research directions for complex traffic management tasks.

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Area of Science:

  • Intelligent Transportation Systems
  • Artificial Intelligence
  • Robotics

Background:

  • Connected and Automated Vehicles (CAVs) necessitate complex, simultaneous task management, including motion planning and traffic control.
  • Multi-Agent Reinforcement Learning (MARL) offers a promising framework for addressing these intricate control problems.
  • Existing research lacks a comprehensive overview of MARL applications in CAVs, hindering further development.

Purpose of the Study:

  • To provide a comprehensive survey of Multi-Agent Reinforcement Learning (MARL) applications in Connected and Automated Vehicles (CAVs).
  • To analyze current research trends, identify key challenges, and propose future research directions in MARL for CAVs.

Main Methods:

  • A classification-based analysis of existing research papers on MARL for CAVs.
  • Identification and categorization of current developments and research directions.
  • Discussion of challenges and potential future research avenues.

Main Results:

  • The survey categorizes current MARL research for CAVs, highlighting diverse approaches and applications.
  • Key challenges in implementing MARL for complex CAV tasks are identified.
  • Potential future research areas are proposed to address identified challenges.

Conclusions:

  • MARL is a critical technology for enabling sophisticated functionalities in CAVs.
  • Further research is needed to overcome current challenges and fully realize MARL's potential in intelligent transportation systems.
  • This survey serves as a valuable resource for researchers and practitioners in the field of MARL for CAVs.