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

When applied to autonomous mobility applications, RL can be used to derive optimal control In the fast-paced world of technological advancements, strategies for maintaining safety, efficiency, and robustness in autonomous mobility has emerged as a transformative innovation, various traffic situations. Furthermore, in order to control the dramatically reshaping numerous aspects of human life, platoon, the use of single-agent RL is not suitable because such as transportation, logistics, and surveillance [1]. These all agents will identically operate when they are located in a complex systems depend on advanced algorithms, sensors, and same space and time with same action-reward settings. Therefore, communication networks to carry out their tasks smoothly for realizing the cooperation and coordination among and proficiently with their own objectives [2]. One crucial multiple agents, multi-agent RL (MARL) algorithms should element that supports the successful functioning of these be utilized [4]-[6]. Among various MARL algorithms, this systems, particularly when operating as a coordinated group, paper considers communication network (CommNet) which is the efficient sharing of information among multiple mobility is widely and actively used in modern distributed computing platforms.