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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Journal ArticleDOI
Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach
TL;DR: In this article, the authors developed a stochastic programming model for metro train rescheduling problem in order to jointly reduce the time delay of affected passengers, their total traveling time and operational costs of trains.
Journal ArticleDOI
A hypothesis for basal ganglia-dependent reinforcement learning in the songbird.
Michale S. Fee,Jesse H. Goldberg +1 more
TL;DR: This work outlines a specific working hypothesis for how BG-forebrain circuits could utilize an internally computed reinforcement signal to direct song learning, and includes a number of general concepts borrowed from the mammalian BG literature, including a dopaminergic reward prediction error and dopamine-mediated plasticity at corticostriatal synapses.
Journal ArticleDOI
Gaze following: why (not) learn it?
TL;DR: A computational model of the emergence of gaze following skills in infant-caregiver interactions is proposed and it is demonstrated that a specific Basic Set of structures and mechanisms is sufficient for gaze following to emerge.
Book
Robot Learning from Human Teachers
Sonia Chernova,Andrea L. Thomaz +1 more
TL;DR: This book provides an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers, and provides best practices for evaluation of LfD systems.
Proceedings Article
Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen,Nan Jiang +1 more
TL;DR: This article revisited these assumptions and provided theoretical results towards answering the above questions, and make steps towards a deeper understanding of value-function approximation, and made steps toward a deeper understand of value function approximation.
References
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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.
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