This paper presents the deep learning-based recent achievements to resolve
the problem of autonomous mobility control and efficient resource management of
autonomous vehicles and UAVs, i.e., (i) multi-agent reinforcement learning
(MARL), and (ii) neural Myerson auction. Representatively, communication
network (CommNet), which is one of the most popular MARL algorithms, is
introduced to enable multiple agents to take actions in a distributed manner
for their shared goals by training all agents' states and actions in a single
neural network. Moreover, the neural Myerson auction guarantees trustfulness
among multiple agents as well as achieves the optimal revenue of highly dynamic
systems. Therefore, we survey the recent studies on autonomous mobility control
based on MARL and neural Myerson auction. Furthermore, we emphasize that
integration of MARL and neural Myerson auction is expected to be critical for
efficient and trustful autonomous mobility services.

近期研究通过深度学习解决自主移动控制和高效资源管理的问题，其中多主体强化学习和神经 Myerson 拍卖是主要方法。研究回顾了基于多主体强化学习和神经 Myerson 拍卖的自主移动控制，同时强调了二者结合的重要性以实现高效而可信的自主移动服务。