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
An Optical Communication's Perspective on Machine Learning and Its Applications
TL;DR: The mathematical foundations of basic ML techniques from communication theory and signal processing perspectives are described, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use.
Posted Content
Off-Policy Actor-Critic
TL;DR: This paper derives an incremental, linear time and space complexity algorithm that includes eligibility traces, proves convergence under assumptions similar to previous off-policy algorithms, and empirically show better or comparable performance to existing algorithms on standard reinforcement-learning benchmark problems.
Journal ArticleDOI
Current and future applications of statistical machine learning algorithms for agricultural machine vision systems
TL;DR: Current application of statistical machine learning techniques in machine vision systems, analyses each technique potential for specific application and represents an overview of instructive examples in different agricultural areas are surveyed.
Journal ArticleDOI
A review On reinforcement learning: Introduction and applications in industrial process control
Rui Nian,Jinfeng Liu,Biao Huang +2 more
TL;DR: An overview of RL along with tutorials for practitioners who are interested in implementing RL solutions into process control applications and a summary of RL’s potential advantages and disadvantages are provided.
Journal ArticleDOI
Autonomous Navigation of UAVs in Large-Scale Complex Environments: A Deep Reinforcement Learning Approach
TL;DR: Experimental results demonstrate that the proposed deep reinforcement learning (DRL) method can enable UAVs to autonomously perform navigation in a virtual large-scale complex environment and can be generalized to more complex, larger-scale, and three-dimensional environments.
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.
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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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