How can I build adaptable, cooperative AI agents?
CORD: Generalizable Cooperation via Role Diversity
This paper introduces CORD, a new method for training cooperative multi-agent AI systems that can generalize their learned behavior to work effectively with new, unseen teammates. It addresses the common problem of overfitting in multi-agent training where agents become too specialized to work with anyone but their original training partners. CORD uses a hierarchical approach with a high-level controller assigning roles to lower-level agents based on maximizing role diversity (influenced by other agents' information). This allows agents to adapt to different team compositions and strategies without prior knowledge of their teammates.
For LLM-based multi-agent systems, CORD offers a promising approach for enhancing generalization and robustness. The concept of role assignment could be valuable in coordinating different LLMs with specialized skills, allowing them to form effective teams dynamically. The focus on maximizing role diversity while considering inter-agent influences aligns with the need for LLMs to collaborate effectively in complex, evolving scenarios. The ability to train without pre-defined policies for new teammates is particularly relevant for open-ended LLM applications where new agents/skills might be introduced dynamically.