The 2nd Coordination and Cooperation in Multi-Agent Reinforcement Learning (CoCoMARL) Workshop

Stimulated by the increasing complexity of real-world problems, multi-agent reinforcement learning (MARL) has emerged as a promising approach for enabling autonomous agents to learn and adapt in dynamic environments. MARL broadly involves multiple autonomous agents learning to interact with each other in their environment, presenting a multitude of unique challenges and opportunities in both fundamental research and practical application.

In this workshop, we aim to focus particularly on MARL problems that require cooperation and/or coordination between multiple autonomous agents to be properly solved. This choice of specialization was made for multiple reasons. First and foremost, cooperative multi-agent systems are continuing to become more and more relevant - seeing effective use in robotic warehousing, space traffic management, and self-driving tasks - we believe this relevancy will only grow further with time. Second, MARL is a vast field of research with a multitude of interesting sub-areas and research problems, thus, it would be most beneficial to the MARL community to dive deep into this one, particularly important category of MARL problems that has a wide range of open problems. Thus, the goal of this workshop is to bring together researchers and practitioners to discuss recent advances, challenges, and applications of MARL, with a particular focus on the following topics:

• Multi-Agent Cooperation/Coordination: Methods and techniques for enabling agents to cooperate/coordinate and achieve goals in complex environments.

• Inter-agent Communication: Approaches for facilitating communication between agents to enhance coordination and decision-making.

• LLMs in MARL: Integration of Large Language Models (LLMs) into MARL systems to enable agents to understand and generate natural language, enhancing their ability to interact with humans and other agents.

• Applications of MARL: Real-world applications of MARL in various domains, such as robotics, autonomous driving, finance, healthcare, and more.

• Game Theory in MARL: Theoretical foundations and practical implications of applying game theoretic concepts in MARL settings.

• Safety in MARL: Techniques and frameworks for ensuring the safety and reliability of MARL systems in critical environments.

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