Results 91 to 100 of about 8,669,620 (261)

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  

Functionalizing Micro‐to‐Mesoscopic Electrode Architectures for Regulating Electron Transfer Behaviors in Electrocatalysis

Advanced Functional Materials, EarlyView.
A systematic review is conducted to assess the influence of electrode architecture across micro‐ to mesoscopic length scales on electron‐transfer pathways in electrocatalysis. We discuss the structure‐activity relationships in electrocatalytic applications, including resource recovery and environmental remediation, and provide cost‐effective, efficient
Manshu Zhao, Jiaojiao Zhang, Shideng Yuan, Xinmeng Yu, Keming Wan, Xiaoguang Duan, Zhining Wang   +6 more
wiley   +1 more source

Industrial design using deep reinforcement learning

, 2018
Deep Reinforcement Learning has developed rapidly in recent years and is likely to be applicable to a variety of real-world problems. In this paper, we examine the problems of industrial design and construct a reinforcement learning environment.
현민성, 곽노준
core   +1 more source

Reservoir‐Driven Neuromorphic Computing Based on Composite Rare‐Earth/Transition Metal Oxide Memristor

Advanced Functional Materials, EarlyView.
A defect‐engineered Ag/Gd2O3:Nb2O5/Pt rare earth composite oxide memristor enables stable multilevel reservoir states through pulse driven conductance modulation. Experimentally measured device responses are incorporated into a device aware reservoir computing framework for CIFAR‐100 image classification, highlighting the potential of rare earth ...
Hammad Ghazanfar, Haider Abbas, Muhammad Rabeel, Honggyun Kim, Ghulam Dastgeer, Muhammad Nasim, Furqan Zahoor, Yawar Abbas, Waqas Ahmad, Deok‐kee Kim   +9 more
wiley   +1 more source

A Comprehensive Study on Reinforcement Learning and Deep Reinforcement Learning Schemes

Sir Syed University Research Journal of Engineering and Technology
Reinforcement learning (RL) has emerged as a powerful tool for creating artificial intelligence systems (AIS) and solving problems which require sequential decision-making. Reinforcement learning has achieved some impressive achievements in recent years,
Muhammad Azhar, Mansoor Ahmed Khuhro, Muhammad Waqas, Umair Saeed, Mehar Khan Niazi   +4 more
doaj   +1 more source

Nitride MXenes Beyond Carbides: Bridging the Gap Between Computational Prediction and Experimental Realization

Advanced Functional Materials, EarlyView.
Nitride MXenes remain constrained by a persistent gap between computational prediction and experimental realization. This Review identifies the thermodynamic, kinetic, and chemical barriers limiting their synthesis, critically evaluates emerging fabrication routes, and proposes a multidimensional computational‐experimental framework to accelerate the ...
Naresh Varnakavi, Masoud Soroush
wiley   +1 more source

The advancements and applications of deep reinforcement learning in Go [PDF]

ITM Web of Conferences
Combining Deep Learning's perceptual skills with Reinforcement Learning's decision-making abilities, Deep Reinforcement Learning (DRL) represents a significant breakthrough in Artificial Intelligence (AI).
Zheng Xutao
doaj   +1 more source

Deep reinforcement learning: Bridging learning and control in intelligent systems

Deep Reinforcement Learning (DRL) has gained popularity as a new approach in artificial intelligence that successfully integrates representation learning through Deep Learning with decision making in Reinforcement Learning.
Şahin, C. B., Raza, A., Zitar, R. A., Abualigah, L., Thanh, H. V., Smerat, A., Al-Okbi, N. K., Liu, Z., Izci, D., Thanh, Hung Vo, Liu, Zhe, Al-Okbi, Nada Khalil, Şahin, Canan Batur, Izci, Davut, Abualigah, Laith, Zitar, Raed Abu   +15 more
core   +1 more source

Mechanics of Telson Ridges in Mantis Shrimp for Impact Analysis and Implications for Bioinspired Elastic Shell Designs

Advanced Functional Materials, EarlyView.
The mantis shrimp telson reveals how ridged biological shields control deformation under impact. Inspired by this architecture, telson‐inspired ridged curved shells transform ridge geometry and macroscopic curvature into bidirectional snap‐through, tunable stiffness, and enhanced hysteretic energy dissipation for multifunctional architected materials ...
Phani Saketh Dasika, Adwait Trikanad, Wei Huang, Taige Hao, David Kisailus, Pablo D. Zavattieri   +5 more
wiley   +1 more source

Programming Neuromorphic Plasticity Through Ionic and Electronic Coupling in Organic Electrochemical Transistors

Advanced Functional Materials, EarlyView.
Neuromorphic plasticity in OECTs is governed by the post‐stimulus evolution of coupled ionic and electronic states. Semiconductor chemistry and morphology, electrolyte composition and interfaces, and device architecture regulate ion injection, transport, confinement, and release, shifting responses from rapidly relaxing STP to metastable memory and ...
Inho Lee, Hyoik Jang, Ji Hwan Kim, Jonghyun Won, Namhee Kim, Wonseok Shin, Giwon Lee, Eunho Lee, Daegun Kim, Sungjun Park   +9 more
wiley   +1 more source