First, I will present a flexible team structure which allows agents to decompose the task space into roles and formations. Second, I will introduce layered learning, a general-purpose machine learning paradigm for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable. Given a hierarchical task decomposition, layered learning allows for learning at every level of the hierarchy, with learning at each level directly affecting learning at the next higher level. TPOT-RL is designed for domains in which agents cannot necessarily observe the state changes when other team members act.
Empirical results validate all three contributions in controlled experiments.
The generality of the contributions is verified by applying them to the real robotic soccer and network routing domains. The talk will also report on results in the RoboCup international competitions.