Results 1 to 10 of about 91 (67)

Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems. [PDF]

Sensors (Basel)
A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm
Xie C, Xiu Y, Yang S, Miao Q, Chen L, Gao Y, Zhang Z.   +6 more
europepmc   +3 more sources

Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models. [PDF]

Polymers (Basel), 2022
Khan K, Iqbal M, Salami BA, Amin MN, Ahamd I, Alabdullah AA, Arab AMA, Jalal FE.   +7 more
europepmc   +1 more source

Ensemble Tree-Based Approach towards Flexural Strength Prediction of FRP Reinforced Concrete Beams. [PDF]

Polymers (Basel), 2022
Amin MN, Iqbal M, Khan K, Qadir MG, Shalabi FI, Jamal A.   +5 more
europepmc   +1 more source

Artist agent:A reinforcemnet learning approach to automatic stroke generation in oriental ink painting

Artist agent:A reinforcemnet learning approach to automatic stroke generation in oriental ink painting
identifier:oai:t2r2.star.titech.ac.jp ...
openaire  

Deep Reinforcemnet Learning: A Promised Way to AI

Proceedings of The 5th IIAE International Conference on Industrial Application Engineering 2017, 2017
openaire   +2 more sources

Algorthmic Trading Strategy Design Using Deep Reinforcemnet Learning

Jaydip Sen, Hetvi Waghela, Rakshit, Sneha, Subhasis Dasgupta   +3 more
openaire   +1 more source

Convergence Analysis on Approximate Reinforcemnet Learning

2007
openaire   +1 more source

Financial incentives to improve adherence to antipsychotic maintenance medication in non-adherent patients: a cluster randomised controlled trial [PDF]

, 2016
Background: Poor adherence to long-term antipsychotic injectable (LAI) medication in patients with psychotic disorders is associated with a range of negative outcomes.
Appleby, Ashcroft, Attkisson, Azrin, Braun, Burns, Burton, Byford, Carey, Claassen, Claassen, Conley, Curtis, Department of Health, Department of Health, Dolan, Dolder, Fenton, Fieve, Gaebel, Gilmer, Giuffrida, Glick, Glover, Gneezy, Gray, Guelfi, Guimon, Guy, Haynes, Health and Social Care Information Centre, Henderson, Higgins, Highton-Williamson, Jeffery, Joint Formulary Committee, Kay, Knapp, Lacro, Macpherson, McDonald, Morisky, National Institute for Health and Care Excellence, Nose, Parrish, Perkins, Pilote, Post, Priebe, Priebe, Priebe, Priebe, Priebe, Priebe, Promberger, Reiss, Reiss, Roll, Ryan, Sato, Shaw, Shepard, Slade, Smith, Staring, Steven-Simon, Streicker, Tidey, Weiden, World Health Organization, Yorkley   +70 more
core   +4 more sources

Self-reflective deep reinforcement learning [PDF]

, 2016
© 2016 IEEE. In this paper we present a new concept of self-reflection learning to support a deep reinforcement learning model. The self-reflective process occurs offline between episodes to help the agent to learn to navigate towards a goal location and
Altahhan, A
core   +1 more source

A Feasibility Study of BBP for predicting shear capacity of FRP reinforced concrete beams without stirrups. [PDF]

, 2016
yesShear failure of concrete elements reinforced with Fiber Reinforced Polymer (FRP) bars is generally brittle, requiring accurate predictions to avoid it.
Ashour, Ashraf F., Golafshani, E.M.
core   +1 more source