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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Metastable Walking Machines
Katie Byl,Russ Tedrake +1 more
TL;DR: This work employs tools from stochastic processes to examine the “stochastic stability” of idealized rimless-wheel and compass-gait walking on randomly generated uneven terrain and designs a controller for an actuated compass gait model which maximizes a measure of stoChastic stability.
Journal ArticleDOI
Recommender Systems Research: A Connection-Centric Survey
TL;DR: It is posited that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data.
Journal ArticleDOI
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Veljko Pejovic,Mirco Musolesi +1 more
TL;DR: A survey of phenomena that mobile phones can infer and predict, and a description of machine learning techniques used for such predictions are presented, paving the way for full-fledged anticipatory mobile computing.
Posted Content
A Tour of Reinforcement Learning: The View from Continuous Control
TL;DR: This article surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications.
Posted Content
Decoupled Neural Interfaces using Synthetic Gradients
Max Jaderberg,Wojciech Marian Czarnecki,Simon Osindero,Oriol Vinyals,Alex Graves,David Silver,Koray Kavukcuoglu +6 more
TL;DR: In this paper, the authors introduce a model of the future computation of the network graph, which predicts what the result of the modelled subgraph will produce using only local information.
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.
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