Scalable Heterogeneous Multiagent Learning from Demonstration

Abstract

We present a method of supervised learning from demonstration for real-time, online training of complex heterogenous multiagent behaviors which scale to large numbers of agents in operation. Our learning method is applicable in domains where coordinated behaviors must be created quickly in unexplored environments. Examples of such problem domains includes disaster relief, search and rescue, and gaming environments. We demonstrate this training method in an adversarial mining scenario which coordinates four types of individual agents to perform six distinct roles in a mining task.