Results 101 to 110 of about 8,669,620 (261)

Reinforcement Learning & Deep REinforcement Learning (Deep Q-Learning)

, 2019
In questo seminario viene introdotto il Reinforcement Learning, poi sono discussi i limiti dei metodi classici per sistemi complessi di grandi dimensioni e viene introdotta la conseguente necessità di sistemi Deep Reinforcement Learning (Deep Q-Learning)
DE CASTRO CRISTINA
core  

Single‐Cell Resolution Mapping of Osteoarthritis‐Associated Chondrocyte Dynamics Using a High‐Density CMOS MEA Platform

Advanced Healthcare Materials, EarlyView.
Single‐cell and spatially resolved impedance mapping using a high‐density CMOS‐MEA platform enables non‐invasive monitoring of osteoarthritis‐associated chondrocyte dynamics. Continuous label‐free measurements reveal heterogeneous remodeling behaviors, including proliferation, migration, and phenotypic transitions, establishing a powerful approach for ...
Neha Deshpande, Pauline Coquart, Bram Theuns, Susanne Cremery, Jaime Rodolfo V. Garcia III, Julien Verplanken, Dennis Lambrechts, Dries Braeken, Liesbet Lagae, Johanna Bolander   +9 more
wiley   +1 more source

Optimal Treatment Strategies for Critical Patients with Deep Reinforcement Learning

Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care.
Li, Lin, Cai, Taotao, Li, Qing, Tao, Xiaohui, Yong, Jianming, Xie, Haoran, Job, Simi   +6 more
core   +1 more source

Microfluidic Engineering of Microgels: A Review of Emerging Technologies, Materials, Fabrication Strategies, and Applications

Advanced Healthcare Materials, EarlyView.
Recent advances in microfluidic technologies for microgel fabrication are critically reviewed, highlighting the capabilities and limitations of major microfluidic platforms and discussing how platform design, material selection, and crosslinking strategies influence microgel architecture and functionality. Current challenges and future opportunities in
Afshin Abrishamkar, Laura Neely, Cynthia Pham, Nada Ibrahim, Alexis Larios‐Cervantes, Ben A. Kertesz, Todd Hoare   +6 more
wiley   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Probability Matching and Reinforcement Learning* [PDF]

Probability matching occurs when an action is chosen with a frequency equivalent to the probability of that action being the best choice. This sub-optimal behavior has been reported repeatedly by psychologist and experimental economist.
Javier Rivas
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A multi-objective deep reinforcement learning framework

, 2020
A multi-objective deep reinforcement learning ...
Peter Vamplew (13459449), Saeid Nahavandi (714568), Ngoc Duy Nguyen (1681423), Chee Peng Lim (13063809), Richard Dazeley (13066251), Thanh Thi Nguyen (13110078)   +5 more
core  

Thermodynamic Limits to Molecular Doping in Conjugated Polymers: A Perspective on Phase Behavior and Miscibility

Advanced Materials, EarlyView.
Molecular doping of conjugated polymers is fundamentally constrained by thermodynamic phase behavior. This Perspective reframes doping efficiency and stability in terms of miscibility limits, binodals, and solvus boundaries, highlighting the role of effective interaction parameters and charge transfer.
Somayeh Kashani, Justin Neu, Sung‐Joo Kwon, Runqio Song, Zixuan Chen, Tajah Trapier, Paschalis Gkoupidenis, David Ginger, Wei You, Jasper J. Michels, Harald Ade   +10 more
wiley   +1 more source

Reinforcement Learning based Recommender System using Q-Learning and Deep Q-Learning

, 2023
The first part of this thesis concludes with an overall summary of the publications so far on the recommender system using reinforcement learning. We have performed a systematic review of the research studies published from 2010-2022.
Rezaei, Mehrdad, NC DOCKS at East Carolina University   +1 more
core   +1 more source