Reinforcement learning - Open Access .click

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, 2001
Without ...
openaire +5 more sources

Quantitative MRI Uncovers Subtle Cortical Damage in Myelin Oligodendrocyte Glycoprotein Antibody‐Associated Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To determine whether myelin‐sensitive quantitative MRI reveals microstructural abnormalities in normal‐appearing cortex (NACtx) in myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD), indicating that conventional MRI underestimates remission residual cortical injury.
Valentina Camera +20 more
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 +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 +3 more
doaj +1 more source

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source

Munchausen Reinforcement Learning

CoRR, 2020
NeurIPS 2020.
Vieillard, Nino, Pietquin, Olivier, Geist, Matthieu +2 more
openaire +4 more sources

Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer

Algorithms
In recent years, with the rapid advancements in Natural Language Processing (NLP) technologies, large models have become widespread. Traditional reinforcement learning algorithms have also started experimenting with language models to optimize training ...
Jiaming Li, Ning Xie, Tingting Zhao
doaj +1 more source

CSF Cytokine Network Organization Predicts Progression Independent of Relapse and MRI Activity in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Progression independent of relapse activity is a major determinant of long‐term disability in multiple sclerosis, but its immunopathologic basis remains incompletely understood. We investigated whether relapse‐independent progression in radiologically stable relapsing–remitting multiple sclerosis is associated with distinct ...
Antonio Bruno +19 more
wiley +1 more source

Reinforcement learning

Astronomy and Computing
To appear, Astronomy & ...
openaire +4 more sources

Boundary‐Dependent Sleep–Wake Dysregulation in Idiopathic Hypersomnia

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Idiopathic hypersomnia (IH) presents with excessive daytime sleepiness (EDS) despite apparently preserved nocturnal sleep, challenging traditional models of hypersomnolence based on sleep loss or fragmentation. We aimed to test the hypothesis that EDS in IH reflects excessive stabilization of the sleep state, consistent with ...
Samantha Mombelli +13 more
wiley +1 more source

deep reinforcement learning
reinforcement learning rl
model-based reinforcement learning

machine learning