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arXiv · 1310.1384

Concurrent learning-based online approximate feedback-Nash equilibrium solution of N-player nonzero-sum differential games

Rushikesh Kamalapurkar·Justin Klotz·Warren E. Dixon
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Abstract

This paper presents a concurrent learning-based actor-critic-identifier architecture to obtain an approximate feedback-Nash equilibrium solution to an infinite horizon N-player nonzero-sum differential game online, without requiring persistence of excitation (PE), for a nonlinear control-affine system. Under a condition milder than PE, uniformly ultimately bounded convergence of the developed control policies to the feedback-Nash equilibrium policies is established.

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Rushikesh Kamalapurkar, Justin Klotz, Warren E. Dixon. 2013-10-04. Concurrent learning-based online approximate feedback-Nash equilibrium solution of N-player nonzero-sum differential games. https://doi.org/10.1109/jas.2014.7004681

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