LfD Training of Heterogeneous Formation Behaviors

Problem domains such as disaster relief, search and rescue, and games can benefit from having a human quickly train coordinated behaviors for a diverse set of agents. Hierarchical Training of Agent Behaviors (HiTAB) is a Learning from Demonstration (LfD) approach that addresses some inherent complexities in multiagent learning, making it possible to train complex heterogeneous behaviors from a small set of training samples. In this paper, we successfully demonstrate LfD training of formation behaviors using a small set of agents that, without retraining, continue to operate correctly when additional agents are available. We selected training of formations for the experiments because formations: require a great deal of coordination between agents, are heterogenous due to the differing roles of participating agents, and can scale as the number of agents grows. We also introduce some extensions to HiTAB that facilitate this type of training.