Title: Model-based reinforcement learning in differential graphical games,
Abstract: This paper seeks to combine differential game theory with the
actor-critic-identifier architecture to determine forward-in-time, approximate
optimal controllers for formation tracking in multi-agent systems, where the
agents have uncertain heterogeneous nonlinear dynamics. A continuous control
strategy is proposed, using communication feedback from extended neighbors on a
communication topology that has a spanning tree. A model-based reinforcement
learning technique is developed to cooperatively control a group of agents to
track a trajectory in a desired formation. Simulation results are presented to
demonstrate the performance of the developed technique.