Solution Sets for Inverse Infinite-Horizon Linear-Quadratic Descriptor Differential Games
This work addresses the inverse problem of identifying cost functions from observed behavior in descriptor differential games, a novel contribution for control and game theory researchers.
The authors characterize the solution set of cost functions that rationalize observed feedback strategies as Nash equilibria in infinite-horizon linear-quadratic descriptor differential games, proving it is rectangular and convex, and provide an algorithm for admissible realizations.
In this letter, we study a model-based inverse problem for infinite-horizon linear-quadratic differential games with descriptor dynamics. Specifically, we seek to identify the set of all cost functions that rationalize an observed feedback strategy profile of the players as a feedback Nash equilibrium, referred to here as the solution set. We characterize the solution set, show that it is rectangular and convex, and provide an algorithm to compute an admissible realization. Finally, we illustrate our results with numerical examples.