Results 71 to 80 of about 387,064 (296)
Multiple Twinning in Nacre and Aragonite
Advanced Functional Materials, EarlyView.Electron backscatter diffraction map of a cluster of geologic aragonite, exhibiting single, double, and triple twins. The whole cluster is approximately 2 cm wide. Colors indicate crystal orientations, so that pixels where the a‐, b‐, and c‐axis is perpendicular to the image plane are green, red, and blue, respectively.
Connor A. Schmidt +7 more
wiley +1 more source
Task-specific effects of reward on task switching
, 2014 Although cognitive control and reinforcement learning have been researched extensively over the last few decades, only recently have studies investigated their interrelationship.
Holroyd, Clay, Umemoto, Akina
core +1 more source
Hierarchical Reinforcement Learning - A Survey [PDF]
International Journal of Computing and Digital Systems, 2015 Reinforcement Learning (RL) has been an interesting research area in Machine Learning and AI. Hierarchical Reinforcement Learning (HRL) that decomposes the RL problem into sub-problems where solving each of which will be more powerful than solving the entire problem will be our concern in this paper.
openaire +1 more source
Advanced Functional Materials, EarlyView.Dry electrode technology revolutionizes battery manufacturing by eliminating toxic solvents and energy‐intensive drying. This work details two promising techniques: dry spray deposition and polymer fibrillation. How their unique solvent‐free bonding mechanisms create uniform microstructures for thicker, denser electrodes, boosting energy density and ...
Yuhao Liang +7 more
wiley +1 more source
On Reinforcement Learning for Full-length Game of StarCraft
, 2019 StarCraft II poses a grand challenge for reinforcement learning. The main difficulties of it include huge state and action space and a long-time horizon. In this paper, we investigate a hierarchical reinforcement learning approach for StarCraft II.
Liu, Ruo-Ze +5 more
core +1 more source
Advanced Functional Materials, EarlyView.This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Benchmarking Deep Reinforcement Learning for Continuous Control [PDF]
, 2016 Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.
Abbeel, Pieter +4 more
core +2 more sources
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
, 2018 Given a text description, most existing semantic parsers synthesize a program in one shot. However, it is quite challenging to produce a correct program solely based on the description, which in reality is often ambiguous or incomplete. In this paper, we
Gao, Jianfeng +4 more
core +1 more source
SHIRO: Soft Hierarchical Reinforcement Learning
, 2022 Hierarchical Reinforcement Learning (HRL) algorithms have been demonstrated to perform well on high-dimensional decision making and robotic control tasks. However, because they solely optimize for rewards, the agent tends to search the same space redundantly. This problem reduces the speed of learning and achieved reward.
Watanabe, Kandai +2 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley +1 more source