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 …

This paper reviews the current state of the art on reinforcement learning (RL)-based
feedback control solutions to optimal regulation and tracking of single and multiagent …

The adaptive dynamic programming (ADP) technology has been widely used benefiting
from its recursive structure in forward and the prospective conception of reinforcement …

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 …

This paper presents a new Reinforcement Learning (RL)-based control approach that uses
the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the …

This paper introduces a novel reference tracking control approach implemented using a
combination of the Actor-Critic Reinforcement Learning (RL) framework and the Grey Wolf …

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 investigates a neural network (NN)‐based control problem for unknown discrete‐
time nonlinear systems with a denial‐of‐service (DoS) attack and an adaptive event …

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 …

In this article, through adaptive critic, a dual event-triggered (DET) constrained control
scheme is established for discrete-time nonlinear zero-sum games. The neural networks are …