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This paper presents an online adaptive optimal control algorithm based on policy iteration reinforcement learning techniques to solve the continuous-time Stackelberg games with infinite horizon for linear systems. This adaptive optimal control method finds in real-time approximations of the optimal value and the Stackelberg-equilibrium solution, while also guaranteeing closed-loop stability. The optimal-adaptive...
Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output...
In this paper we present an online gaming algorithm based on policy iteration to solve the continuous-time (CT) two-player zero-sum game with infinite horizon cost for nonlinear systems with known dynamics. That is, the algorithm learns online in real-time the solution to the game design HJI equation. This method finds in real-time suitable approximations of the optimal value, and the saddle point...
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