August 18, 2024Open Access

Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

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Authors

RZRuiqi ZhangJiangsu University JHJing HouCalifornia State University, San Marcos FWFlorian WalterFraunhofer Institute for Telecommunications, Heinrich Hertz Institute

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Implication

Review demonstrates structured simulator metrics and algorithm designs in multi-agent autonomous driving systems, highlighting scalable coordination across intelligent transportation.

Key Points

Multi-agent reinforcement learning enables decentralized sequential decision-making for autonomous driving, but it substantially augments computational complexity and policy design.
Review of simulation platforms establishes standardized benchmark metrics, while categorizing state representation, perception units, and environmental modeling architectures.
Analysis highlights open deployment challenges across intelligent transportation systems, emphasizing realistic simulator data and scalable multi-agent coordination frameworks.

Cite This Study

Zhang et al. (2024) studied this question.

synapsesocial.com/papers/68e5bd40b6db6435875554b6 https://doi.org/10.48550/arxiv.2408.09675

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Implication

Review demonstrates structured simulator metrics and algorithm designs in multi-agent autonomous driving systems, highlighting scalable coordination across intelligent transportation.

Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Discussion

Member takes

Implication

Key Points

Cite This Study

Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Discussion

Member takes

Implication

Key Points

Cite This Study

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