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 …

In this paper, the resilient practical cooperative output regulation problem (CORP) is
addressed for heterogeneous linear multi-agent systems with unknown switching exosystem …

In a paper by Willems et al., it was shown that persistently exciting data can be used to
represent the input-output behavior of a linear system. Based on this fundamental result, we …

In this paper, a novel online mode-free integral reinforcement learning algorithm is proposed
to solve the multiplayer non-zero sum games. We first collect and learn the subsystems …

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

This article presents a model-free-policy iteration (-PI) for the discrete-time linear quadratic
regulation (LQR) problem. To solve the algebraic Riccati equation arising from solving the …

Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

This article presents a learning‐based barrier certified method to learn safe optimal
controllers that guarantee operation of safety‐critical systems within their safe regions while …

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each
other when performing intelligent optimization. They are both regarded as promising …