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Reinforcement learning
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581	Markov Decision Processes and the Modelling of Patient Flows Anthony Clissold Supervised by Professor Jerzy Filar Flinders University 1 Add to Reading List Source URL: vrs.amsi.org.au Language: English - Date: 2014-09-04 22:39:09 Markov chain Markov decision process Stochastic matrix Matrix Reinforcement learning Models of DNA evolution Statistics Markov models Markov processes
582	Pervasive Multimedia for Autism Intervention S. Venkatesh,† S. Greenhill,‡ D. Phung,† B. Adams,‡ and Thi Duong† † Centre for Pattern Recognition and Data Analytics (PRaDA), School of Information Technology, D Add to Reading List Source URL: www.deakin.edu.au Language: English - Date: 2012-07-04 23:14:21 Psychiatry Developmental psychology Autism Applied psychology Cognitive behavioral therapy Applied behavior analysis Reinforcement Learning Play therapy Psychology Behaviorism Mind
583	The use of aversive stimuli in horse training Ethical equitation is coming into sharp focus in equestrian culture. Concurrently, the alarming death and serious injury statistics have been highlighted in scientific litera Add to Reading List Source URL: equitationscience.businesscatalyst.com Language: English - Date: 2013-07-14 08:26:48 Philosophy of psychology Equitation science Operant conditioning Reinforcement Aversives Punishment Equitation Learning Shaping Behaviorism Behavior Ethology
584	1368 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 8, AUGUST 2009 Simple Artificial Neural Networks That Match Probability and Exploit and Explore When Confronting Add to Reading List Source URL: www.bcp.psych.ualberta.ca Language: English - Date: 2012-07-11 15:01:06 Behavior Statistics Neural networks Machine learning Perceptron Artificial neural network Operant conditioning Reinforcement Matching law Behaviorism Learning Computational neuroscience
585	Greedy Algorithms for Sparse Reinforcement Learning Christopher Painter-Wakefield Ronald Parr Duke University, Durham NC Add to Reading List Source URL: www.cs.duke.edu Language: English - Date: 2012-05-22 14:26:52 Signal processing Mathematical optimization Regression analysis Multivariate statistics Matching pursuit Linear algebra Least-angle regression Sparse approximation Reinforcement learning Statistics Mathematics Applied mathematics
586	Learning and Motivation[removed]–103 www.elsevier.com/locate/l&m The implications of null patterns and output unit activation functions on simulation studies of learning: A case study of patterning Add to Reading List Source URL: www.bcp.psych.ualberta.ca Language: English - Date: 2012-07-11 15:01:15 Cybernetics Neuroscience Cognitive science Learning Perceptron Multilayer perceptron Connectionism Reinforcement Activation function Neural networks Computational neuroscience Science
587	Greedy Algorithms for Sparse Reinforcement Learning Christopher Painter-Wakefield Ronald Parr Duke University, Durham NC Add to Reading List Source URL: www.cs.duke.edu Language: English - Date: 2012-05-22 14:27:13 Regression analysis Learning Reinforcement learning Sparse approximation Supervised learning Feature selection Least squares Least-angle regression Static single assignment form Statistics Matching pursuit Signal processing
588	Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs Gavin Taylor Department of Computer Science United States Naval Academy Add to Reading List Source URL: www.cs.duke.edu Language: English - Date: 2012-06-25 15:53:46 Gaussian function Signal processing Image processing Nonparametric regression Least squares Reinforcement learning Kernel density estimation Smoothing K-nearest neighbor algorithm Statistics Regression analysis Non-parametric statistics
589	An Experimental Comparison of Classical, FOND and Probabilistic Planning Andreas Hertle, Christian Dornhege, Thomas Keller, Robert Mattm¨ uller, Manuela Ortlieb, and Bernhard Nebel University of Freiburg, 79110 Freiburg Add to Reading List Source URL: gki.informatik.uni-freiburg.de Language: English - Date: 2014-07-07 07:15:35 Markov processes Stochastic control Planning Motion planning Markov decision process Reinforcement learning Statistics Automated planning and scheduling Dynamic programming
590	Generalized Value Functions for Large Action Sets Jason Pazis Ronald Parr Department of Computer Science, Duke University, Durham, NC[removed]USA Abstract Add to Reading List Source URL: www.cs.duke.edu Language: English - Date: 2011-05-11 14:49:02 Dynamic programming Abstract algebra Linear algebra Stochastic control Markov processes Markov decision process Reinforcement learning Function Q-learning Mathematics Algebra Statistics

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