Real World Multi Agent

Real-world multi-agent systems research focuses on developing algorithms enabling effective collaboration among multiple agents with diverse capabilities and limited information, mirroring complexities found in autonomous driving or robotics teams. Current efforts concentrate on reinforcement learning frameworks, often incorporating techniques like value-based factorization, distributed rollout algorithms, and adaptive action supervision to improve coordination and efficiency in various scenarios. These advancements are crucial for creating robust and scalable multi-agent systems applicable to diverse real-world applications, addressing challenges such as zero-shot scalability and the handling of normative disagreements in mixed-motive environments. The ultimate goal is to design systems that can learn and adapt to complex, dynamic environments without centralized control.