How can I train agents for diverse teamwork?
Hierarchical Imitation Learning of Team Behavior from Heterogeneous Demonstrations
This paper introduces DTIL, a new algorithm for training AI agents to work together in teams, learning from examples of how humans collaborate. It focuses on scenarios where teams might perform the same task in different ways and where agents have limited information about their teammates. DTIL uses a hierarchical structure, similar to how humans break down complex tasks into smaller subtasks, and improves upon existing methods by handling diverse team strategies and partial observability.
For LLM-based multi-agent systems, DTIL offers a way to train agents that can exhibit flexible and diverse collaboration strategies learned from human demonstrations, even when agents have only partial views of the overall task. This is particularly relevant for complex, real-world applications where explicit communication may be limited or undesirable, making flexible coordination through learned subtask structures a valuable approach. DTIL's use of non-adversarial imitation learning could also potentially address some of the training instability often encountered in generative adversarial imitation learning methods.