Multi Agent Network

Multi-agent networks study systems of interconnected agents collaboratively solving tasks, aiming to optimize performance and robustness. Current research emphasizes developing efficient algorithms for distributed consensus, resource allocation, and learning, often employing techniques like alternating direction method of multipliers (ADMM), Bayesian optimization, and various reinforcement learning approaches tailored to decentralized architectures. These advancements are crucial for addressing challenges in areas such as autonomous driving, large-scale sensor networks, and distributed optimization problems, improving efficiency and scalability in complex systems.