Results 71 to 80 of about 34,803,504 (287)
Consistency of fuzzy model-based reinforcement learning [PDF]
2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), 2008 Reinforcement learning (RL) is a widely used paradigm for learning control. Computing exact RL solutions is generally only possible when process states and control actions take values in a small discrete set. In practice, approximate algorithms are necessary.
Lucian Busoniu +3 more
openaire +2 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio +8 more
wiley +1 more source
Multi-model based reinforcement learning and application
, 2020 Decker, Keith S.Reinforcement learning (RL) as a category of machine learning methods learns sequential decision-making policies by interacting with the environment and maximizing a cumulative reward.
Wei, Haoran
core +1 more source
Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani +10 more
wiley +1 more source
Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer
AlgorithmsIn 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
Model-based average reward reinforcement learning
Artificial Intelligence, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Prasad Tadepalli, DoKyeong Ok
openaire +3 more sources
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
Fuzzy and tile coding approximation techniques for coevolution in reinforcement learning [PDF]
, 2005 PhDThis thesis investigates reinforcement learning algorithms suitable for learning in large state space problems and coevolution. In order to learn in large state spaces, the state space must be collapsed to a computationally feasible size and then ...
Tokarchuk, Laurissa Nadia
core +4 more sources
IEEE Access, 2020 In this paper, we proposed a novel Hybrid Reinforcement Learning framework to maintain the stability of a biped robot (NAO) while it is walking on static and dynamic platforms.
Ao Xi, Chao Chen
doaj +1 more source
Model-Based Reinforcement Learning for Sepsis Treatment
CoRR, 2018 Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we explore the use of continuous state-space model-based reinforcement learning (RL)
Aniruddh Raghu +2 more
openaire +3 more sources