RLC 2026
Coordination and Cooperation in Multi-Agent Reinforcement Learning
Anaïs Berkes, Sydney Dolan, Siddharth Nayak, Ethan Rathbun, Kyle Tilbury, Jianhong Wang
About This Workshop
Multi-agent reinforcement learning (MARL) has emerged as a promising approach for enabling autonomous agents to learn and adapt in dynamic environments. This workshop focuses on MARL problems that require cooperation and/or coordination between multiple autonomous agents.
Cooperative multi-agent systems are increasingly relevant in robotic warehousing, space traffic management, and self-driving tasks. Topics include multi-agent cooperation/coordination methods, inter-agent communication, LLMs in MARL, real-world applications, game theory in MARL, and safety in MARL.