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.
@article{arxiv.1702.08584,
title = {Model-based reinforcement learning in differential graphical games},
author = {Rushikesh Kamalapurkar and Justin R. Klotz and Patrick Walters and Warren E. Dixon},
journal= {arXiv preprint arXiv:1702.08584},
year = {2017}
}