How to scale multi-agent control for networks?
Scalable spectral representations for network multiagent control
This research paper tackles the challenge of creating scalable multi-agent reinforcement learning algorithms for systems with continuous states and actions, particularly when those agents are arranged in a network structure.
The key insight is that by leveraging the exponential decay property of network dynamics and using spectral representations of local transition probabilities, one can create efficient representations of individual agent behavior (Q-functions). This allows for efficient learning and control, even in large networks with complex individual agents, as might be the case with LLM-powered agents. The paper provides both theoretical guarantees for this approach and demonstrates its effectiveness with simulated examples of thermal control in a multi-zone building and Kuramoto oscillator synchronization.