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
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Artificial Intelligence: Foundations of Computational Agents
L Poole David,K Mackworth Alan +1 more
TL;DR: The book balances theory and experiment, showing how to link them intimately together, and develops the science of AI together with its engineering applications, to encapsulate the latest results without being exhaustive and encyclopedic.
Journal ArticleDOI
Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling.
TL;DR: The authors construct a TDRL model that can accommodate extinction and renewal through two simple processes: a T DRL process that learns the value of situation-action pairs and a situation recognition process that categorizes the observed cues into situations.
Journal ArticleDOI
The Orbitofrontal Oracle: Cortical Mechanisms for the Prediction and Evaluation of Specific Behavioral Outcomes
TL;DR: New findings indicate that OFC provides predictions about specific outcomes associated with stimuli, choices, and actions, especially their moment-to-moment value based on current internal states.
Journal ArticleDOI
Accuracy-based learning classifier systems: models, analysis and applications to classification tasks
TL;DR: This paper investigates two models of accuracy-based learning classifier systems on different types of classification problems, and provides a model on the learning complexity of LCS which is based on the representative examples given to the system.
Proceedings ArticleDOI
Towards the Science of Security and Privacy in Machine Learning
TL;DR: In this article, a comprehensive threat model for ML, and categorize attacks and defenses within an adversarial framework are presented. And the authors formally explore the opposing relationship between model accuracy and resilience to adversarial manipulation.
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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