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This paper presents the deep learning-based recent achievements to resolvethe problem of autonomous mobility control and efficient resource management ofautonomous vehicles and UAVs, i.e., (i) multi-agent reinforcement learning(MARL), and (ii) neural Myerson auction. Representatively, communicationnetwork (CommNet), which is one of the most popular MARL algorithms, isintroduced to enable multiple agents to take actions in a distributed mannerfor their shared goals by training all agents' states and actions in a singleneural network. Moreover, the neural Myerson auction guarantees trustfulnessamong multiple agents as well as achieves the optimal revenue of highly dynamicsystems. Therefore, we survey the recent studies on autonomous mobility controlbased on MARL and neural Myerson auction. Furthermore, we emphasize thatintegration of MARL and neural Myerson auction is expected to be critical forefficient and trustful autonomous mobility services.

Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes