Results 101 to 110 of about 1,052,376 (334)

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

A new approach for drone tracking with drone using Proximal Policy Optimization based distributed deep reinforcement learning

SoftwareX, 2023
In this paper, a distributed deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed for an unmanned aerial vehicle (UAV) to autonomously track another UAV. Accordingly, this paper makes three important contributions
Ziya Tan, Mehmet Karaköse
doaj   +1 more source

Zein‐Based Adhesives: Sustainable Extraction and Application in Bioadhesive Technologies

Advanced Engineering Materials, EarlyView.
Zein is extracted from corn gluten meal using a simple and scalable process with high yield (~90%). The resulting protein is applied in bioadhesives modified with Ca2+ and Fe3+ ions, exhibiting substrate‐dependent adhesion. The findings demonstrate competitive bonding performance and highlight the role of ionic interactions in tuning adhesion ...
Paula Bertolino Sanvezzo, Daniela Winsley Valereto Friozi, Vinícius de Oliveira, Marcia Cristina Branciforti   +3 more
wiley   +1 more source

Applications of Deep Learning and Reinforcement Learning to Biological Data [PDF]

, 2018
Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine ...
Vassanelli, S, Amir Hussain, Kaiser, MS, Hussain, Amir, Mahmud, Mufti, Kaiser, Mohammed Shamim, Vassanelli, Stefano, Hussain, A, Hussain A., Mahmud M., Mohammed Shamim Kaiser, Kaiser M. S., Mufti Mahmud, Stefano Vassanelli, Mahmud, M, Vassanelli S.   +15 more
core   +1 more source

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

IEEE Access, 2020
The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely,
Marwan Qaid Mohammed, Kwek Lee Chung, Chua Shing Chyi   +2 more
doaj   +1 more source

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Journal of Intelligent and Robotic Systems
Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios ...
Hoangcong Le, Saeed Saeedvand, Chen-Chien Hsu   +2 more
semanticscholar   +1 more source

Deep Learning Wizard

, 2019
<p>This code base contains the entire website's assets of Deep Learning Wizard covering deep learning theories, concepts and Python, C++, and PyTorch code.</p> <p>This release contains stable versions for deep learning ...
Ritchie Ng, Jie Fu
core   +1 more source

Sharing Knowledge in Multi-Task Deep Reinforcement Learning [PDF]

, 2020
We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption that learning from different tasks, sharing common properties, is helpful to ...
A. Bonarini, D`Eramo, C., M. Restelli, Bonarini, A., D. Tateo, Tateo, D., J. Peters, Restelli, M., Peters, J., C. D'Eramo   +9 more
core  

Thermodynamic Pathways of Nonequilibrium Solidification in Wire‐Arc Additive Manufacturing Fe‐Based Multicomponent Alloy Structures

Advanced Engineering Materials, EarlyView.
Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios, Anthony Gonzalez, Aleksander Aleman, Tyler Dolmetsch, Tanaji Paul, Arvind Agarwal   +5 more
wiley   +1 more source