Can I read A Supplementary Condition for the Convergence of the Control Policy during Adaptive Dynamic Programming on EtoBox?

A Supplementary Condition for the Convergence of the Control Policy during Adaptive Dynamic Programming by Bao, Xuefeng; Mao, Zhi-Hong; Sharma, Nitin is a scholarly article available to read on EtoBox.

What is A Supplementary Condition for the Convergence of the Control Policy during Adaptive Dynamic Programming about?

Reinforcement learning based adaptive/approximate dynamic programming (ADP) is a powerful technique to determine an approximate optimal controller for a dynamical system. These methods bypass the need to analytically solve the nonlinear Hamilton-Jacobi-Bellman equation, whose solution is often to difficult to determine but is needed to determine the optimal control policy. ADP methods usually employ a policy iteration algorithm that evaluates and improves a value function at every step to find the optimal control policy. Previous works in ADP have been lacking a stronger condition that ensures the convergence of the policy iteration algorithm. This paper provides a sufficient but not necessary condition that guarantees the convergence of an ADP algorithm. This condition may provide a more solid theoretical framework for ADP-based control algorithm design for nonlinear dynamical systems.

Author

Bao, Xuefeng; Mao, Zhi-Hong; Sharma, Nitin

Published

2018

Language

EN