Large Scale Multi Agent

Large-scale multi-agent systems (LMAS) research focuses on designing and controlling systems with numerous interacting agents, aiming to achieve efficient coordination and collective behavior. Current efforts concentrate on developing scalable algorithms, such as decentralized reinforcement learning methods (e.g., federated Q-learning, independent Q-learning), hierarchical control architectures, and novel communication frameworks (e.g., pheromone-inspired communication), to overcome challenges posed by the complexity and heterogeneity of LMAS. This field is crucial for advancing applications in robotics, traffic management, and other domains requiring the coordinated control of large numbers of autonomous entities, with recent work emphasizing both theoretical guarantees and practical performance improvements.