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arXiv · 1702.08584

Model-based reinforcement learning in differential graphical games

Rushikesh Kamalapurkar·Justin R. Klotz·Patrick Walters·Warren E. Dixon
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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.

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Rushikesh Kamalapurkar, Justin R. Klotz, Patrick Walters, Warren E. Dixon. 2017-02-28. Model-based reinforcement learning in differential graphical games. https://doi.org/10.1109/tcns.2016.2617622

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