Results 81 to 90 of about 404,230 (315)

A Hierarchical Framework for Relation Extraction with Reinforcement Learning

, 2018
Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modeled.
Huang, Minlie, Liu, Jiexi, Takanobu, Ryuichi, Zhang, Tianyang   +3 more
core   +1 more source

Engineering Strategies for Stable and Long‐Life Alkaline Zinc‐Based Flow Batteries

Advanced Functional Materials, EarlyView.
Alkaline zinc‐based flow batteries face persistent challenges from unstable zinc deposition, including dendrite growth, passivation, corrosion, and hydrogen evolution, which severely limit cycling stability. Current research addresses these issues through coordinated electrode structuring, electrolyte regulation, and membrane design to control zinc ...
Yuran Bai, Xiaoyu Huo, Xin Long, Enkang Fu, Xusong Gong, Lei Wei, Liang An   +6 more
wiley   +1 more source

Regionally Localized Electrolyte Engineering via UV Crosslinking for Stable Ether–Carbonate Lithium Metal Batteries

Advanced Functional Materials, EarlyView.
This work introduces a regionally localized electrolyte (RLE) that spatially separates ether‐ and carbonate‐based functions to stabilize both Li metal and high‐voltage cathodes. An immobilized ether‐rich layer directs Li+ transport, activates LiNO3 locally, and forms a uniform LiF‐rich SEI, enabling lower overpotential, uniform deposition, and long ...
Eunbin Lim, Jeanie Pearl Dizon Suba, Jaegu Cho, Sukeun Yoon, Kuk Young Cho   +4 more
wiley   +1 more source

Temporal-adaptive Hierarchical Reinforcement Learning

, 2020
Hierarchical reinforcement learning (HRL) helps address large-scale and sparse reward issues in reinforcement learning. In HRL, the policy model has an inner representation structured in levels. With this structure, the reinforcement learning task is expected to be decomposed into corresponding levels with sub-tasks, and thus the learning can be more ...
Zhou, Wen-Ji, Yu, Yang
openaire   +2 more sources

A hierarchical reinforcement learning method for persistent time-sensitive tasks [PDF]

, 2016
Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight. However, little effort has been put into developing methods to learn policies for complex persistent tasks and tasks ...
Belta, Calin, Li, Xiao
core  

Bioprinting Organs—Science or Fiction?—A Review From Students to Students

Advanced Healthcare Materials, EarlyView.
Bioprinting artificial organs has the potential to revolutionize the medical field. This is a comprehensive review of the bioprinting workflow delving into the latest advancements in bioinks, materials and bioprinting techniques, exploring the critical stages of tissue maturation and functionality.
Nicoletta Murenu, Camilla Mussoni, Mateo S. Andrade Mier, Paula Buettner, Nathaly Chicaiza‐Cabezas, Yi‐Yu Robin Dai, Jessica Faber, Maren Fiedler, Zan Lamberger, Xuen Jen Ng, Vanessa Moessler, Anna Rederer, Jonas Roeder, Sabrina Stecher, Katinka Theis, Jeanette Weigelt, Silvia Budday, Gregor Lang, Natascha Schaefer   +18 more
wiley   +1 more source

Target-Oriented Multi-Agent Coordination with Hierarchical Reinforcement Learning

Applied Sciences
In target-oriented multi-agent tasks, agents collaboratively achieve goals defined by specific objects, or targets, in their environment. The key to success is the effective coordination between agents and these targets, especially in dynamic ...
Yuekang Yu, Zhongyi Zhai, Weikun Li, Jianyu Ma   +3 more
doaj   +1 more source

Local Goals Driven Hierarchical Reinforcement Learning [PDF]

, 2004
* This research was partially supported by the Latvian Science Foundation under grant No.02-86d.Efficient exploration is of fundamental importance for autonomous agents that learn to act.
Pchelkin, Arthur
core  

Precise Control of Drug Release in Machine Learning‐Designed Antibody‐Eluting Implants for Postoperative Scarring Inhibition in Glaucoma

Advanced Healthcare Materials, EarlyView.
We developed a micro‐sized, biocompatible implant for postoperative sustained delivery of anti‐fibrotic antibodies in glaucoma surgery. Machine learning‐guided optimization of polymer composition, implant geometry, and porosity enabled precise control of drug release.
Mengqi Qin, Wenbing Jiang, Kai Xin Thong, Zeynep Ulker, Brihitejas Patel, Cynthia Yu‐Wai‐Man   +5 more
wiley   +1 more source

A Hierarchical Signal-to-Policy Learning Framework for Risk-Aware Portfolio Optimization

International Journal of Financial Studies
This study proposes a hierarchical signal-to-policy learning framework for risk-aware portfolio optimization that integrates model-based return forecasting, explainable machine learning, and deep reinforcement learning (DRL) within a unified architecture.
Jiayang Yu, Kuo-Chu Chang
doaj   +1 more source