期刊
ETRI JOURNAL
卷 45, 期 5, 页码 735-745出版社
WILEY
DOI: 10.4218/etrij.2023-0132
关键词
auction; autonomous mobility control; deep learning; platoon; reinforcement learning
This paper surveys recent efforts in multiagent reinforcement learning and neural Myerson auction deep learning to improve mobility control and resource management in autonomous vehicles. The findings suggest that integrating MARL CommNet and Myerson techniques is essential for improved efficiency and trustworthiness.
This paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness.
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