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Machine learning in medical applications: A review of state-of-the-art methods
Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …
complex challenges in recent years in various application areas, such as medical, financial …
Adaptive dynamic programming for control: A survey and recent advances
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 …
applications in control. First, its applications in optimal regulation are introduced, and some …
Adaptive multi-step evaluation design with stability guarantee for discrete-time optimal learning control
This paper is concerned with a novel integrated multi-step heuristic dynamic programming
(MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the …
(MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the …
Discounted iterative adaptive critic designs with novel stability analysis for tracking control
The core task of tracking control is to make the controlled plant track a desired trajectory. The
traditional performance index used in previous studies cannot eliminate completely the …
traditional performance index used in previous studies cannot eliminate completely the …
Reinforcement learning-based decentralized fault tolerant control for constrained interconnected nonlinear systems
This paper addresses the decentralized fault tolerant control problem for interconnected
nonlinear systems under a reinforcement learning strategy. The system under consideration …
nonlinear systems under a reinforcement learning strategy. The system under consideration …
Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems
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 …
to solve the multiplayer non-zero sum games. We first collect and learn the subsystems …
Optimal and autonomous control using reinforcement learning: A survey
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 …
feedback control solutions to optimal regulation and tracking of single and multiagent …
Model-Free λ-Policy Iteration for Discrete-Time Linear Quadratic Regulation
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 …
regulation (LQR) problem. To solve the algebraic Riccati equation arising from solving the …
Safe reinforcement learning using robust MPC
Reinforcement learning (RL) has recently impressed the world with stunning results in
various applications. While the potential of RL is now well established, many critical aspects …
various applications. While the potential of RL is now well established, many critical aspects …
Reinforcement learning for control: Performance, stability, and deep approximators
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …