Multi Agent Coordination

Multi-agent coordination research focuses on enabling groups of agents, whether robots, AI models, or software entities, to collaboratively achieve complex tasks. Current research emphasizes improving coordination efficiency through various techniques, including decentralized execution with locally centralized control, graph neural networks for representing agent interactions and communication, and the integration of large language models for planning and communication. This field is crucial for advancing autonomous systems, improving the performance of multi-agent reinforcement learning, and enabling robust and scalable solutions for applications ranging from robotics and traffic control to resource management and game playing.