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
Data-based approximate policy iteration for affine nonlinear continuous-time optimal control design
TL;DR: This paper addresses the model-free nonlinear optimal control problem based on data by introducing the reinforcement learning (RL) technique by using a data-based approximate policy iteration (API) method by using real system data rather than a system model.
Journal ArticleDOI
Deliberation for autonomous robots: A survey
Félix Ingrand,Malik Ghallab +1 more
TL;DR: A global overview of deliberation functions in robotics is presented and the main characteristics, design choices and constraints of these functions are discussed.
Journal ArticleDOI
PHY-Layer Spoofing Detection with Reinforcement Learning in Wireless Networks
TL;DR: Spoofing detection schemes based on Q-learning and Dyna-Q are proposed, which achieve the optimal test threshold in the spoofing detection via reinforcement learning and are implemented over universal software radio peripherals and evaluated via experiments in indoor environments.
Journal ArticleDOI
Experience Replay for Real-Time Reinforcement Learning Control
TL;DR: This paper evaluates ER RL on real-time control experiments that involve a pendulum swing-up problem and the vision-based control of a goalkeeper robot, and develops a general ER framework that can be combined with essentially any incremental RL technique, and instantiate this framework for the approximate Q-learning and SARSA algorithms.
Journal ArticleDOI
Lateral habenula neurons signal errors in the prediction of reward information
TL;DR: It is hypothesized that information-seeking is assigned value by the same circuits that support reward-seeking, such that neural signals encoding reward prediction errors include analogous information prediction errors (IPEs).
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.
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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.
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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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