How to train agents to form and move efficiently?
MFC-EQ: Mean-Field Control with Envelope Q-learning for Moving Decentralized Agents in Formation
October 17, 2024
https://arxiv.org/pdf/2410.12062This research tackles the challenge of coordinating multiple decentralized agents to reach their goals efficiently while maintaining a desired formation, known as Moving Agents in Formation (MAIF). The key innovation is MFC-EQ, a system that uses:
- Mean-field reinforcement learning: To simplify interactions between agents by having each agent react to the average effect of its neighbors, improving scalability to large-scale scenarios.
- Envelope Q-learning: To learn a single policy adaptable to different priorities between minimizing the time taken and maintaining the formation, vital for applications with varying objectives.
This is particularly relevant to LLM-based multi-agent systems as it provides a mechanism for coordinating many LLM agents with limited communication, enabling them to work together effectively on complex tasks.