How can nucleolus credit assignment improve multi-agent RL coalitions?
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning
This paper proposes a new way to assign credit (rewards) in multi-agent reinforcement learning (MARL) based on the "nucleolus" concept from cooperative game theory. Instead of assuming all agents work together in one big team, this method allows agents to form smaller, more effective sub-teams (coalitions) to tackle parts of a complex task. This results in faster learning and better performance, especially in difficult scenarios.
For LLM-based multi-agent systems, this research suggests that organizing LLMs into dynamic sub-teams, with fair reward distribution amongst them, could improve efficiency and performance on complex tasks. The nucleolus-based approach provides a theoretical framework for creating stable and interpretable coalition structures in multi-agent LLM applications.