Open AccessBook
Reinforcement Learning: An Introduction
TLDR
This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.Abstract:
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.read more
Citations
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Proceedings Article
A Distributional Perspective on Reinforcement Learning
TL;DR: This paper argues for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent, and designs a new algorithm which applies Bellman's equation to the learning of approximate value distributions.
Book ChapterDOI
Ant Colony Optimization: Overview and Recent Advances
Marco Dorigo,Thomas Stützle +1 more
TL;DR: This chapter reviews developments in ACO and gives an overview of recent research trends, including the development of high-performing algorithmic variants and theoretical understanding of properties of ACO algorithms.
Journal ArticleDOI
Speech Recognition Using Deep Neural Networks: A Systematic Review
TL;DR: A thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications is provided.
Journal ArticleDOI
The dynamics of perception and action.
TL;DR: A theoretical framework called behavioral dynamics is developed that integrates an information-based approach to perception with a dynamical systems approach to action for a given task, which is used to develop theories of several tasks in which a human agent interacts with the physical environment.
Proceedings Article
Deep Recurrent Q-Learning for Partially Observable MDPs
Matthew Hausknecht,Peter Stone +1 more
TL;DR: Deep Recurrent Q-Network (DRQN) as discussed by the authors replaces the first post-convolutional fully-connected layer with a recurrent LSTM, which integrates information through time and replicates DQN's performance on standard Atari games and partially observed equivalents featuring flickering game screens.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Related Papers (5)
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Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more