Decentralized Multi Agent

Decentralized multi-agent systems research focuses on designing and analyzing systems where multiple autonomous agents collaborate to achieve a common goal without relying on a central controller. Current research emphasizes robust algorithms, such as those based on reinforcement learning, control barrier functions, and distributed optimization (including variations of ADMM), to handle uncertainty, safety constraints, and limited communication. These advancements are crucial for applications ranging from robotics and autonomous driving to smart cities and distributed energy systems, enabling efficient and resilient coordination in complex environments. The field is actively exploring methods to improve efficiency, safety, and privacy in these decentralized systems.