Can I use symmetries to improve MARL scalability?
Symmetries-enhanced Multi-Agent Reinforcement Learning
This paper introduces a novel method for improving multi-agent reinforcement learning (MARL) by leveraging symmetries, even when the system itself doesn't possess them. It proposes embedding the system within a larger, symmetrical one, learning a policy within that enhanced system, and then projecting the learned policy back onto the original system. This approach simplifies policy learning and improves generalization.
For LLM-based multi-agent systems, this research offers a potential path to enhance scalability and generalization by incorporating symmetries into agent interactions and training, even if the underlying environment or application domain is not inherently symmetrical. The "Group Equivariant Graphormer" architecture proposed could be adapted to incorporate the structure and symmetries of information flow within LLM-based multi-agent communication. This could potentially streamline the learning process and facilitate more complex, coordinated behaviors among LLM agents.