Results 51 to 60 of about 323,609 (233)

A new approach for drone tracking with drone using Proximal Policy Optimization based distributed deep reinforcement learning

SoftwareX, 2023
In this paper, a distributed deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed for an unmanned aerial vehicle (UAV) to autonomously track another UAV. Accordingly, this paper makes three important contributions
Ziya Tan, Mehmet Karaköse
doaj   +1 more source

Engineering a QoS Provider Mechanism for Edge Computing with Deep Reinforcement Learning

, 2019
With the development of new system solutions that integrate traditional cloud computing with the edge/fog computing paradigm, dynamic optimization of service execution has become a challenge due to the edge computing resources being more distributed and ...
Carpio, Francisco, Ferrer, Ana Juan, Jukan, Admela, Sosa, Roman   +3 more
core   +1 more source

Evaluation of a novel EHR sidecar application to display RA clinical outcomes during clinic visits: results of a stepped‐wedge cluster randomized pragmatic trial

Arthritis Care &Research, Accepted Article.
Objective We developed a novel EHR sidecar application to visualize key rheumatoid arthritis (RA) outcomes, including disease activity, physical function, and pain, via a patient‐facing graphical interface designed for use during outpatient visits (“RA PRO dashboard”).
Gabriela Schmajuk, Jing Li, Cathy Nasrallah, Cherish Wilson, Alicia Hamblin, Christine Hariz, Cammie Young, Stephen Shiboski, Emma Kersey, Mary Nakamura, Judith F. Ashouri, Mehrdad Matloubian, Andrew J. Gross, Jennifer Barton, Beth Berrean, Jonathan Prugh, Jinoos Yazdany   +16 more
wiley   +1 more source

Aero-Engine Modeling and Control Method with Model-Based Deep Reinforcement Learning

Aerospace, 2023
Due to the strong representation ability and capability of learning from data measurements, deep reinforcement learning has emerged as a powerful control method, especially for nonlinear systems, such as the aero-engine control system.
Wenbo Gao, Muxuan Pan, Wenxiang Zhou, Feng Lu, Jin-Quan Huang   +4 more
doaj   +1 more source

Playing Atari with Deep Reinforcement Learning [PDF]

, 2013
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.
Antonoglou, Ioannis, Graves, Alex, Kavukcuoglu, Koray, Mnih, Volodymyr, Riedmiller, Martin, Silver, David, Wierstra, Daan   +6 more
core   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

Quality of service optimization algorithm based on deep reinforcement learning in software defined network

物联网学报, 2023
Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have ...
Cenhuishan LIAO, Junyan CHEN, Guanping LIANG, Xiaolan XIE, Xiaoye LU   +4 more
doaj   +2 more sources

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

IEEE Access, 2020
The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely,
Marwan Qaid Mohammed, Kwek Lee Chung, Chua Shing Chyi   +2 more
doaj   +1 more source

Advancing Electronic Application of Coordination Solids: Enhancing Electron Transport and Device Integration via Surface‐Mounted MOFs (SURMOFs)

Advanced Functional Materials, EarlyView.
The layer‐by‐layer (LbL) assembly of coordination solids, enabled by the surface‐mounted metal‐organic framework (SURMOF) platform, is on the cusp of generating the organic counterpart of the epitaxy of inorganics. The programmable and sequential SURMOF protocol, optimized by machine learning (ML), is suited for accessing high‐quality thin films of ...
Zhengtao Xu, Christof Wöll, Stefan Bräse   +2 more
wiley   +1 more source

Pretraining in Deep Reinforcement Learning: A Survey [PDF]

, 2022
Zhihui Xie, Zichuan Lin, Junyou Li, Shuai Li, Deheng Ye   +4 more
openalex   +1 more source