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 …
Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges
In the last few years, there has been an exponential increase in the usage of the
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …
Attention, please! A survey of neural attention models in deep learning
A de Santana Correia, EL Colombini - Artificial Intelligence Review, 2022 - Springer
In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …
limited ability to process competing sources, attention mechanisms select, modulate, and …
Adaptive multigradient recursive reinforcement learning event-triggered tracking control for multiagent systems
This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning
(RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent …
(RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent …
Data-driven performance-prescribed reinforcement learning control of an unmanned surface vehicle
N Wang, Y Gao, X Zhang - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
An unmanned surface vehicle (USV) under complicated marine environments can hardly be
modeled well such that model-based optimal control approaches become infeasible. In this …
modeled well such that model-based optimal control approaches become infeasible. In this …
Reinforcement learning-based optimal tracking control of an unknown unmanned surface vehicle
In this article, a novel reinforcement learning-based optimal tracking control (RLOTC)
scheme is established for an unmanned surface vehicle (USV) in the presence of complex …
scheme is established for an unmanned surface vehicle (USV) in the presence of complex …
Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems
A Boukerche, Y Tao, P Sun - Computer networks, 2020 - Elsevier
In recent years, the Intelligent transportations system (ITS) has received considerable
attention, due to higher demands for road safety and efficiency in highly interconnected road …
attention, due to higher demands for road safety and efficiency in highly interconnected road …
Semisupervised deep reinforcement learning in support of IoT and smart city services
M Mohammadi, A Al-Fuqaha… - IEEE Internet of Things …, 2017 - ieeexplore.ieee.org
Smart services are an important element of the smart cities and the Internet of Things (IoT)
ecosystems where the intelligence behind the services is obtained and improved through …
ecosystems where the intelligence behind the services is obtained and improved through …
NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems
W Bai, T Li, S Tong - IEEE Transactions on Cybernetics, 2020 - ieeexplore.ieee.org
This article investigates an adaptive reinforcement learning (RL) optimal control design
problem for a class of nonstrict-feedback discrete-time systems. Based on the neural …
problem for a class of nonstrict-feedback discrete-time systems. Based on the neural …
Deep reinforcement learning-based automatic exploration for navigation in unknown environment
This paper investigates the automatic exploration problem under the unknown environment,
which is the key point of applying the robotic system to some social tasks. The solution to this …
which is the key point of applying the robotic system to some social tasks. The solution to this …