Results 51 to 60 of about 197,676 (281)

Discrete Event Modeling and Simulation for Reinforcement Learning System Design

Information, 2022
Discrete event modeling and simulation and reinforcement learning are two frameworks suited for cyberphysical system design, which, when combined, can give powerful tools for system optimization or decision making process for example.
Laurent Capocchi, Jean-François Santucci   +1 more
doaj   +1 more source

Navigating new norms: a systematic review of factors for the development of effective digital tools in higher education

FEBS Open Bio, EarlyView.
What factors make for an effective digital learning tool in Higher Education? This systematic review identifies elements of a digital tool that published examples reveal to be features of an engaging and impactful digital tool. A systematic literature search yielded 25 research papers for analysis.
Akmal Arzeman, Jessica Haines, Connie Pritchard, Stephen Rutherford, Nigel Francis   +4 more
wiley   +1 more source

Relational Reinforcement Learning

, 2001
This paper presents an introduction to reinforcement learning and relational reinforcement learning at a level to be understood by students and researchers with different backgrounds.It gives an overview of the fundamental principles and techniques of reinforcement learning without involving a rigorous deduction of the mathematics involved through the ...
openaire   +3 more sources

Enzymatic degradation of biopolymers in amorphous and molten states: mechanisms and applications

FEBS Open Bio, EarlyView.
This review explains how polymer morphology and thermal state shape enzymatic degradation pathways, comparing amorphous and molten biopolymer structures. By integrating structure–reactivity principles with insights from thermodynamics and enzyme engineering, it highlights mechanisms that enable efficient polymer breakdown.
Anđela Pustak, Aleksandra Maršavelski
wiley   +1 more source

GenFedRL: a general federated reinforcement learning framework for deep reinforcement learning agents

Tongxin xuebao, 2023
To solve the problem that intelligent devices equipped with deep reinforcement learning agents lack effective security data sharing mechanisms in the intelligent Internet of things, a general federated reinforcement learning (GenFedRL) framework was ...
Biao JIN, Yikang LI, Zhiqiang YAO, Yulin CHEN, Jinbo XIONG   +4 more
doaj   +2 more sources

Comparative Analysis of Energy Management Strategies for HEV: Dynamic Programming and Reinforcement Learning

IEEE Access, 2020
Energy management strategy is an important factor in determining the fuel economy of hybrid electric vehicles; thus, much research on how to distribute the required power to engines and motors of hybrid vehicles is required.
Heeyun Lee, Changhee Song, Namwook Kim, Suk Won Cha   +3 more
doaj   +1 more source

Higher Amyloid and Tau Burden Is Associated With Faster Decline on a Digital Cognitive Test

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective A 2‐min digital clock‐drawing test (DCTclock) captures more granular features of the clock‐drawing process than the pencil‐and‐paper clock‐drawing test, revealing more subtle deficits at the preclinical stage of Alzheimer's disease (AD). A previous cross‐sectional study demonstrated that worse DCTclock performance was associated with
Jessie Fanglu Fu, Talia Robinson, Marina Rodriguez Alonso, Adela Francis Malave, Roos J. Jutten, Michael J. Properzi, Brian C. Healy, Jackson C. Thompson, Grace Del Carmen Montenegro, Emma Thibault, Dana Penney, Randall Davis, Reisa A. Sperling, Kathryn V. Papp, Keith A. Johnson, Julie C. Price, Dorene M. Rentz   +16 more
wiley   +1 more source

Reinforcement Learning

, 2019
Reinforcement Learning (RL) is one of the model free machine learning algorithms where the agent learns its behaviours from the environment by actually interacting with it. This is better than the offline planner because the agent actually interacts with the environment to learn its behaviours because it is almost impossible to simulate a real world in
Jimut Bahan Pal, Debadri Chatterjee, Sounak Modak   +2 more
openaire   +3 more sources

Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient‐Reported Measures

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein, Haoqi Sun, Sophie Lehnerer, Lea Gerischer, Maximilian Mönch, Christian Meisel, Andreas Meisel, Pushpa Narayanaswami   +7 more
wiley   +1 more source

A Scoping Review on Artificial Intelligence–Supported Interventions for Nonpharmacologic Management of Chronic Rheumatic Diseases

Arthritis Care &Research, EarlyView.
This review summarizes artificial intelligence (AI)‐supported nonpharmacological interventions for adults with chronic rheumatic diseases, detailing their components, purpose, and current evidence base. We searched Embase, PubMed, Cochrane, and Scopus databases for studies describing AI‐supported interventions for adults with chronic rheumatic diseases.
Nirali Shah, Alexis Castellanos, Yen Chen, John Piette, Amy Bucher, Susan Murphy   +5 more
wiley   +1 more source