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 is concerned with the stability of the closed-loop system using various control
policies generated by value iteration. Some stability properties involving admissibility …

In this article, evolving and incremental value iteration (VI) frameworks are constructed to
address the discrete-time zero-sum game problem. First, the evolving scheme means that …

Deep deterministic policy gradient (DDPG) is a powerful reinforcement learning algorithm for
large-scale continuous controls. DDPG runs the back-propagation from the state-action …

Gradient-based algorithms are commonly used for training radial basis function neural
network (RBFNN). However, it is still difficult to avoid vanishing gradient to improve the …

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 …

This article presents an optimal evolution strategy for continuous strategy games on complex
networks via reinforcement learning (RL). In the past, evolutionary game theory usually …

In this article, the problem of the event-triggered optimal control for a class of strict-feedback
nonlinear systems with external disturbances is investigated. First, by introducing proper low …

This article investigates optimal control for a class of large-scale systems using a data-driven
method. The existing control methods for large-scale systems in this context separately …

In this paper, the tracking control problem of unknown nonlinear systems is solved by using
the generalized N‐step value gradient learning algorithm with parameter λ λ GNSVGL (λ λ) …