Coordination Problem

The coordination problem in multi-agent systems focuses on enabling multiple independent agents to work together effectively towards a shared goal, despite limitations like partial observability and decentralized decision-making. Current research emphasizes developing robust offline multi-agent reinforcement learning (MARL) methods, employing model-based approaches and algorithms like proximal policy optimization, and leveraging techniques such as graph neural networks and transformer architectures to improve coordination strategies. These advancements are crucial for real-world applications ranging from robotics and autonomous driving to human-robot collaboration and complex socio-technical systems, where efficient coordination is paramount for optimal performance and safety.