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

ABSTRACT: Connected and automated vehicles (CAVs) require multiple tasks in their seamless maneuverings. Some essential tasks that require simultaneous management and actions are motion planning, traffic prediction, traffic intersection management, etc. A few of them are complex in nature. Multi-agent reinforcement learning (MARL) can solve complex problems involving simultaneous controls. Recently, many researchers applied MARL in such applications. However, there is a lack of extensive surveys on the ongoing research to identify the current problems, proposed methods, and future research directions in MARL for CAVs. This paper provides a comprehensive survey on MARL for CAVs. A classification-based paper analysis is performed to identify the current developments and highlight the various existing

SUBMITTER: Yadav P 

PROVIDER: S-EPMC10221654 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature