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
Triple Dissociation of Information Processing in Dorsal Striatum, Ventral Striatum, and Hippocampus on a Learned Spatial Decision Task
Matthijs A. A. van der Meer,Adam Johnson,Adam Johnson,Neil Schmitzer-Torbert,Neil Schmitzer-Torbert,A. David Redish +5 more
TL;DR: A comparative methodology is developed in order to use a comparative methodology and apply the same analyses to all three structures and suggests that the different roles these structures play are due to differences in information-processing mechanisms.
Journal ArticleDOI
Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends
Matthew Veres,Medhat Moussa +1 more
TL;DR: This paper presents a survey that highlights the role modeling techniques within the realm of deep learning have played within ITS, focusing on how practitioners have formulated problems to address these various challenges, and outline both architectural and problem-specific considerations used to develop solutions.
Journal ArticleDOI
Intelligent Offloading in Multi-Access Edge Computing: A State-of-the-Art Review and Framework
TL;DR: The basic concept of MEC and main applications are introduced, and existing fundamental works using various ML-based approaches are reviewed, and some potential issues of AI in MEC for future work are discussed.
Proceedings ArticleDOI
An adaptive pursuit strategy for allocating operator probabilities
TL;DR: In this article, the adaptive pursuit method is proposed to learn the optimal probability of applying an exploration operator from a set of alternatives. But the adaptive allocation strategy remains sensitive to changes in the reward distributions, and reacts swiftly to non-stationary shifts in the environment.
Proceedings Article
Dendritic cortical microcircuits approximate the backpropagation algorithm
TL;DR: A novel view of learning on dendritic cortical circuits and on how the brain may solve the long-standing synaptic credit assignment problem is introduced, in which error-driven synaptic plasticity adapts the network towards a global desired output.
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.
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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