A Supplementary Condition for the Convergence of the Control Policy during Adaptive Dynamic Programming

TL;DR

This paper introduces a new sufficient condition that guarantees the convergence of adaptive dynamic programming algorithms, enhancing the theoretical foundation for control policy optimization in nonlinear systems.

Contribution

It provides a novel sufficient condition for the convergence of ADP policy iteration algorithms, strengthening the theoretical basis for nonlinear control design.

Findings

Established a new convergence condition for ADP algorithms.

Enhanced theoretical understanding of ADP stability.

Potentially improved control policy reliability.

Abstract

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.