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

This article reviews the recent development of adaptive dynamic programming (ADP) with
applications in control. First, its applications in optimal regulation are introduced, and some …

In this article, we propose a way to enhance the learning framework for zero-sum games
with dynamics evolving in continuous time. In contrast to the conventional centralized actor …

This article presents a novel efficient experience-replay-based adaptive dynamic
programming (ADP) for the optimal control problem of a class of nonlinear dynamical …

The fault-tolerant control problem of a hydraulic servo actuator in the presence of actuator
faults is studied utilizing adaptive dynamic programming. This task is challenging because of …

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 …

Q-learning, a type of reinforcement learning, has gained increasing popularity in
autonomous mobile robot path planning recently, due to its self-learning ability without …

Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great
power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex …

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 …

With the rapid development in information science and technology, many businesses and
industries have undergone great changes, such as chemical industry, electric power …