Approximating Solutions for Nonlinear Dynamic Tracking Games
This paper presents the OPTGAME algorithm developed to iteratively approximate equilibrium solutions of tracking games, i.e. discrete-time nonzero-sum dynamic games with a finite number of players who face quadratic objective functions. Such a tracking game describes the behavior of decision makers who act upon a nonlinear discrete-time dynamical system, and who aim at minimizing the deviations from individually desirable paths of multiple states over a joint finite planning horizon. Among the noncooperative solution concepts, the OPTGAME algorithm approximates feedback Nash and Stackelberg equilibrium solutions, and the open-loop Nash solution, and the cooperative Pareto-optimal solution.
algorithm stackelberg equilibrium dynamic game theory noncooperative game cooperative game tracking problem nash equilibrium markov-perfect equilibrium pareto optimum