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 …

强化学习作为一种用于解决无模型序列决策问题的方法已经有数十年的历史,
但强化学习方法在处理高维变量问题时常常会面临巨大挑战. 近年来, 深度学习迅猛发展 …

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 …