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
Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control
TL;DR: In this article, a fully scalable and decentralized MARL algorithm for the state-of-the-art deep RL agent, advantage actor critic (A2C), within the context of adaptive traffic signal control (ATSC) is presented.
Journal ArticleDOI
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
TL;DR: This work introduces a recurrent deep neural network for real-time financial signal representation and trading and proposes a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training.
Journal ArticleDOI
What does dopamine mean
TL;DR: An alternative account of how dopamine regulates ongoing behavior is described, which proposes a model that explains how dopamine can serve as both a learning signal and as a critical modulator of motivated decision-making.
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The Behavioral Theory of the Firm: Assessment and Prospects
TL;DR: The Behavioral Theory of the Firm has had an enormous influence on organizational theory, strategic management, and neighboring fields of socio-scientific inquiry as discussed by the authors, and its central concepts have become...
Journal ArticleDOI
Don't fear 'fear conditioning': Methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear
Tina B. Lonsdorf,Mareike M. Menz,Marta Andreatta,Miguel A. Fullana,Armita Golkar,Jan Haaker,Ivo Heitland,Andrea Hermann,Manuel Kuhn,Onno Kruse,Shira Meir Drexler,Ann Meulders,Frauke Nees,Andre Pittig,Jan Richter,Sonja Römer,Youssef Shiban,Anja Schmitz,Benjamin Straube,Bram Vervliet,Julia Wendt,Johanna M.P. Baas,Christian J. Merz +22 more
TL;DR: This compendium is intended to provide a basis for the development of a common procedural and terminology framework for the field of human fear conditioning and provides evidence‐based guidance for methodological decisions on study design, outcome measures, and analyses.
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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