The field of multi-agent systems is witnessing significant developments in coordination and decision-making mechanisms. Recent studies have highlighted the importance of considering communication delays, memory, and routing loops in autonomous vehicle routing. The use of distributed optimization and adaptive penalty parameters has been shown to improve coordination performance in multi-robot systems. Additionally, value-of-information aware low-latency communication schemes have been proposed to mitigate the effects of communication latency in multi-agent reinforcement learning systems. Noteworthy papers in this area include:

• The introduction of Object Memory Management (OMM) to prevent routing loops and improve travel time in autonomous vehicle routing.
• The development of Delay-Aware ADMM (DA-ADMM) for asynchronous distributed multi-robot motion planning under imperfect communication.
• The proposal of Value-of-Information aware Low-latency Communication (VIL2C) scheme for enhancing performance in collaborative multi-agent reinforcement learning systems.