Optimal control problems are ubiquitous in practical engineering applications and social life
with the idea of cost or resource conservation. Based on the critic learning scheme, adaptive …

This article studies the adaptive optimal control problem for continuous-time nonlinear
systems described by differential equations. A key strategy is to exploit the value iteration …

The idea of optimization can be regarded as an important basis of many disciplines and
hence is extremely useful for a large number of research fields, particularly for artificial …

Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …

Policy iteration and value iteration are two main iterative adaptive dynamic programming
frameworks for solving optimal control problems. Policy iteration converges fast while …

In this article, the general value iteration (GVI) algorithm for discrete-time zero-sum games is
investigated. The theoretical analysis focuses on stability properties of the systems and also …

In this paper, a novel successive approximation framework, named hybrid iteration (HI), is
proposed to fill up the performance gap between two well-known dynamic programming …

This paper is concerned with a novel integrated multi-step heuristic dynamic programming
(MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the …

Reinforcement learning has proved to be a powerful tool to solve optimal control problems
over the past few years. However, the data-based constrained optimal control problem of …

The optimal control problem of discrete-time nonlinear systems depends on the solution of
the Bellman equation. In this paper, an adaptive reinforcement learning (RL) method is …