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
Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning
Hamidreza Modares,Frank L. Lewis +1 more
TL;DR: An online learning algorithm is developed to solve the linear quadratic tracking (LQT) problem for partially-unknown continuous-time systems and it is shown that the value function is Quadratic in terms of the state of the system and the command generator.
Journal ArticleDOI
Speed/accuracy trade-off between the habitual and the goal-directed processes.
TL;DR: A normative model for arbitration between the two processes that makes an approximately optimal balance between search-time and accuracy in decision making is proposed and can explain experimental evidence on behavioural sensitivity to outcome at the early stages of learning, but insensitivity at the later stages.
Journal ArticleDOI
Frontal Cortex Subregions Play Distinct Roles in Choices between Actions and Stimuli
Peter H. Rudebeck,Timothy E.J. Behrens,Steven W. Kennerley,Mark G. Baxter,Mark J. Buckley,Mark E. Walton,Matthew F. S. Rushworth +6 more
TL;DR: The effect of selective OFC lesions on how monkeys used the history of reinforcement to guide choices of either particular actions or particular stimuli was studied and compared with the effects of ACCS lesions, which were found to cause a more general learning impairment in more naturalistic situations in which reward was stochastic.
Journal ArticleDOI
Neural Prediction Errors Reveal a Risk-Sensitive Reinforcement-Learning Process in the Human Brain
TL;DR: This article used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk and found that the learned values of cues that predict rewards of equal mean but different variance are indeed modulated by experienced risk.
Proceedings ArticleDOI
Learning to Drive in a Day
Alex Kendall,Jeffrey Hawke,David Janz,Przemysław Mazur,Daniele Reda,John-Mark Allen,Vinh-Dieu Lam,Alex Bewley,Amar Shah +8 more
TL;DR: In this paper, the authors demonstrate the first application of deep reinforcement learning to autonomous driving using a single monocular image as input, and provide a general and easy to obtain reward: the distance travelled by the vehicle without the safety driver taking control.
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.
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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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