Multiagent Reinforcement Learning: Methods, Trustworthiness, Applications in Intelligent Vehicles, and Challenges

IF 14 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-06-03 DOI:10.1109/TIV.2024.3408257

Ziyuan Zhou;Guanjun Liu;Ying Tang

引用次数: 0

Abstract

Multiagent Reinforcement Learning (MARL) plays a pivotal role in intelligent vehicle systems, offering solutions for complex decision-making, coordination, and adaptive behavior among autonomous agents. This review aims to highlight the importance of fostering trust in MARL and emphasize the significance of MARL in revolutionizing intelligent vehicle systems. First, this paper summarizes the fundamental methods of MARL. Second, it identifies the limitations of MARL in safety, robustness, generalization, and ethical constraints and outlines the corresponding research methods. Then we summarize their applications in intelligent vehicle systems. Considering human interaction is essential to practical applications of MARL in various domains, the paper also analyzes the challenges associated with MARL's applications in human-machine systems. These challenges, when overcome, could significantly enhance the real-world implementation of MARL-based intelligent vehicle systems.

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多智能体强化学习：方法、可信度、在智能车辆中的应用和挑战

多智能体强化学习（MARL）在智能汽车系统中发挥着关键作用，为自主智能体之间复杂的决策、协调和自适应行为提供了解决方案。本文旨在强调培养对MARL的信任的重要性，并强调MARL在智能汽车系统革命中的意义。本文首先总结了MARL的基本方法。其次，它确定了MARL在安全性，稳健性，泛化和伦理约束方面的局限性，并概述了相应的研究方法。然后总结了它们在智能汽车系统中的应用。考虑到人机交互对于MARL在各个领域的实际应用至关重要，本文还分析了MARL在人机系统中的应用所面临的挑战。这些挑战一旦被克服，将大大增强基于marl的智能车辆系统在现实世界中的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Intelligent Vehicles Mathematics-Control and Optimization

CiteScore

12.10

自引率

13.40%

发文量

177

期刊介绍： The IEEE Transactions on Intelligent Vehicles (T-IV) is a premier platform for publishing peer-reviewed articles that present innovative research concepts, application results, significant theoretical findings, and application case studies in the field of intelligent vehicles. With a particular emphasis on automated vehicles within roadway environments, T-IV aims to raise awareness of pressing research and application challenges. Our focus is on providing critical information to the intelligent vehicle community, serving as a dissemination vehicle for IEEE ITS Society members and others interested in learning about the state-of-the-art developments and progress in research and applications related to intelligent vehicles. Join us in advancing knowledge and innovation in this dynamic field.