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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Intrinsically Motivated Learning in Natural and Artificial Systems
TL;DR: This book introduces the concept of intrinsic motivation in artificial systems, reviews the relevant literature, offers insights from the neural and behavioural sciences, and presents novel tools for research.
Posted Content
Value Iteration Networks
TL;DR: The Value Iteration Network (VIN) as discussed by the authors is a differentiable approximation of the value iteration algorithm, which can be represented as a convolutional neural network and trained end-to-end using standard backpropagation.
Journal ArticleDOI
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
Renzhi Lu,Seung Ho Hong +1 more
TL;DR: Simulation results show that this proposed incentive-based demand response algorithm induces demand side participation, promotes service provider and customers profitabilities, and improves system reliability by balancing energy resources, which can be regarded as a win-win strategy for both service providers and customers.
Journal ArticleDOI
Learning latent structure: Carving nature at its joints
Samuel J. Gershman,Yael Niv +1 more
TL;DR: An emerging literature on 'structure learning'--using experience to infer the structure of a task--and how this can be of service to RL, with an emphasis on structure in perception and action is surveyed.
Journal ArticleDOI
Neural computations underlying action-based decision making in the human brain.
TL;DR: Using fMRI in human subjects, evidence is found for action-value signals in supplementary motor cortex and signals in the dorsomedial frontal cortex that resemble the output of a decision comparator, which implicates this region in the computation of the decision itself.
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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