Twin Deterministic Policy Gradient Adaptive Dynamic Programming for Optimal Control of Affine Nonlinear Discrete-time Systems

Recent achievements in the field of adaptive dynamic programming (ADP), as well as the data resources and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control technologies. This paper proposes a twin deterministic policy gradient adaptive dynamic programming (TDPGADP) algorithm to solve the optimal control problem for a discrete-time affine nonlinear system in a model-free scenario. To solve the overestimation problem resulted from function approximation errors, the minimum value between the double Q network is taken to update the control policy. The convergence of the proposed algorithm in which the value function is served as the Lyapunov function is verified. By designing a twin actor-critic network structure, combining the target network and a specially designed adaptive experience replay mechanism, the algorithm is convenient to implement and the sample efficiency of the learning process can be improved. Two simulation examples are conducted to verify the efficacy of the proposed method.