Results 91 to 100 of about 404,230 (315)

On Credit Assignment in Hierarchical Reinforcement Learning

, 2022
Hierarchical Reinforcement Learning (HRL) has held longstanding promise to advance reinforcement learning. Yet, it has remained a considerable challenge to develop practical algorithms that exhibit some of these promises. To improve our fundamental understanding of HRL, we investigate hierarchical credit assignment from the perspective of conventional ...
Vries, J.A. de, Moerland, T.M., Plaat, A.   +2 more
openaire   +3 more sources

Hierarchical Reinforcement Learning for Quadruped Locomotion [PDF]

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
Legged locomotion is a challenging task for learning algorithms, especially when the task requires a diverse set of primitive behaviors. To solve these problems, we introduce a hierarchical framework to automatically decompose complex locomotion tasks.
Jain, Deepali, Iscen, Atil, Caluwaerts, Ken   +2 more
openaire   +2 more sources

Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling

Advanced Healthcare Materials, EarlyView.
Emerging advances in three‐dimensional bioprinting and computational modeling are reshaping cardiovascular (CV) research by enabling more realistic, patient‐specific tissue platforms. This review surveys cutting‐edge approaches that merge biomimetic CV constructs with computational simulations to overcome the limitations of traditional models, improve ...
Tanmay Mukherjee, Mehdi Salar Amoli, Sarah Rezapourdamanab, Lama Rita El Shammas, Martin L. Tomov, Emilio A. Mendiola, Vahid Serpooshan, Reza Avazmohammadi   +7 more
wiley   +1 more source

Laser‐Assisted Processing and Modification of Bioactive Glasses: A Review

Advanced Healthcare Materials, EarlyView.
Laser technologies provide powerful tools to process and transform bioactive glasses for advanced biomedical applications. This review discusses laser‐matter interaction mechanisms, laser surface engineering, and laser‐assisted fabrication of scaffolds and nanofibers.
Antonio Riveiro, Mario González‐Quintas, Mónica Fernández‐Arias, Jesús del Val, Félix Quintero, Rafael Comesaña, Fernando Lusquiños, Julian R. Jones, Juan Pou   +8 more
wiley   +1 more source

Evaluation of the Dual Impact of Nanotechnologies on Health and Environment Through Alternative Bridging Models

Advanced Healthcare Materials, EarlyView.
This review explores how alternative invertebrate and small‐vertebrate models advance the evaluation of nanomaterials across medicine and environmental science. By bridging cellular and organismal levels, these models enable integrated assessment of toxicity, biodistribution, and therapeutic performance.
Marie Celine Lefevre, Margherita Bernardeschi, Matteo Battaglini, Gianni Ciofani   +3 more
wiley   +1 more source

Success-efficient/failure-safe strategy for hierarchical reinforcement motor learning.

PLoS Computational Biology
Our study explores how ecological aspects of motor learning enhance survival by improving movement efficiency and mitigating injury risks during task failures.
Jan Babič, Tjasa Kunavar, Erhan Oztop, Mitsuo Kawato   +3 more
doaj   +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

State Abstraction in MAXQ Hierarchical Reinforcement Learning

, 1999
Many researchers have explored methods for hierarchical reinforcement learning (RL) with temporal abstractions, in which abstract actions are defined that can perform many primitive actions before terminating. However, little is known about learning with
Dietterich, Thomas G.
core   +1 more source

Machine Learning–Assisted Bio‐Interfacial Engineering Resolves Structural–Functional Conflicts in Nanocomposites

Advanced Materials, EarlyView.
A machine learning‐guided bio‐interfacial design strategy resolves the long‐standing strength–toughness–functionality trade‐off in nanocomposites. By efficiently mapping high‐performance regions in the composition–processing space, the approach delivers hierarchically entangled, nanosheet‐pinned architectures that combine mechanical robustness ...
Hao Wang, Xianfeng Chen, Peiyao Yan, Siqi Liu, Biaobiao Yan, Junhua Kong, Siew Lang Teo, Kai Jin, Jie Zhang, Ping Koy Lam, Chaobin He   +10 more
wiley   +1 more source

Classifying Options for Deep Reinforcement Learning

, 2016
In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the ...
Arulkumaran, Kai, Bharath, Anil Anthony, Dilokthanakul, Nat, Shanahan, Murray   +3 more
core