Risk Sensitive And Efficient Reinforcement Learning Algorithms

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Algorithms for Reinforcement Learning

Author : Csaba Grossi
Publisher : Springer Nature
ISBN 13 : 3031015517
Total Pages : 89 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Algorithms for Reinforcement Learning by : Csaba Grossi

Download or read book Algorithms for Reinforcement Learning written by Csaba Grossi and published by Springer Nature. This book was released on 2022-05-31 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration

Artificial Intelligence for Risk Management

Author : Archie Addo
Publisher : Business Expert Press
ISBN 13 : 1949443523
Total Pages : 128 pages
Book Rating : 4.9/5 (494 download)

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Book Synopsis Artificial Intelligence for Risk Management by : Archie Addo

Download or read book Artificial Intelligence for Risk Management written by Archie Addo and published by Business Expert Press. This book was released on 2020-03-13 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) for Risk Management is about using AI to manage risk in the corporate environment. The content of this work focuses on concepts, principles, and practical applications that are relevant to the corporate and technology environments. The authors introduce AI and discuss the different types, capabilities, and purposes–including challenges. With AI also comes risk. This book defines risk, provides examples, and includes information on the risk-management process. Having a solid knowledge base for an AI project is key and this book will help readers define the knowledge base needed for an AI project by developing and identifying objectives of the risk-knowledge base and knowledge acquisition for risk. This book will help you become a contributor on an AI team and learn how to tell a compelling story with AI to drive business action on risk.

Risk-Sensitive Optimal Control

Author : Peter Whittle
Publisher :
ISBN 13 :
Total Pages : 266 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Risk-Sensitive Optimal Control by : Peter Whittle

Download or read book Risk-Sensitive Optimal Control written by Peter Whittle and published by . This book was released on 1990-05-11 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two major themes of this book are risk-sensitive control and path-integral or Hamiltonian formulation. It covers risk-sensitive certainty-equivalence principles, the consequent extension of the conventional LQG treatment and the path-integral formulation.

Reinforcement Learning, second edition

Author : Richard S. Sutton
Publisher : MIT Press
ISBN 13 : 0262352702
Total Pages : 549 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Reinforcement Learning, second edition by : Richard S. Sutton

Download or read book Reinforcement Learning, second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. 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 while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Robotics

Author : Nicholas Roy
Publisher : MIT Press
ISBN 13 : 0262315734
Total Pages : 501 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Robotics by : Nicholas Roy

Download or read book Robotics written by Nicholas Roy and published by MIT Press. This book was released on 2013-07-05 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Papers from a flagship conference reflect the latest developments in the field, including work in such rapidly advancing areas as human-robot interaction and formal methods. Robotics: Science and Systems VIII spans a wide spectrum of robotics, bringing together contributions from researchers working on the mathematical foundations of robotics, robotics applications, and analysis of robotics systems. This volume presents the proceedings of the eighth annual Robotics: Science and Systems (RSS) conference, held in July 2012 at the University of Sydney. The contributions reflect the exciting diversity of the field, presenting the best, the newest, and the most challenging work on such topics as mechanisms, kinematics, dynamics and control, human-robot interaction and human-centered systems, distributed systems, mobile systems and mobility, manipulation, field robotics, medical robotics, biological robotics, robot perception, and estimation and learning in robotic systems. The conference and its proceedings reflect not only the tremendous growth of robotics as a discipline but also the desire in the robotics community for a flagship event at which the best of the research in the field can be presented.

Machine Learning: ECML 2003

Author : Nada Lavrač
Publisher : Springer Science & Business Media
ISBN 13 : 3540201211
Total Pages : 521 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Machine Learning: ECML 2003 by : Nada Lavrač

Download or read book Machine Learning: ECML 2003 written by Nada Lavrač and published by Springer Science & Business Media. This book was released on 2003-09-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th European Conference on Machine Learning, ECML 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with PKDD 2003. The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for PKDD 2003, selected from a total of 332 submissions. The papers address all current issues in machine learning including support vector machine, inductive inference, feature selection algorithms, reinforcement learning, preference learning, probabilistic grammatical inference, decision tree learning, clustering, classification, agent learning, Markov networks, boosting, statistical parsing, Bayesian learning, supervised learning, and multi-instance learning.

Understanding Machine Learning

Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
ISBN 13 : 1107057132
Total Pages : 415 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Distributional Reinforcement Learning

Author : Marc G. Bellemare
Publisher : MIT Press
ISBN 13 : 0262048019
Total Pages : 385 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Distributional Reinforcement Learning by : Marc G. Bellemare

Download or read book Distributional Reinforcement Learning written by Marc G. Bellemare and published by MIT Press. This book was released on 2023-05-30 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment. The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions.

ECAI 2010

Author : European Coordinating Committee for Artificial Intelligence
Publisher : IOS Press
ISBN 13 : 160750605X
Total Pages : 1184 pages
Book Rating : 4.6/5 (75 download)

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Book Synopsis ECAI 2010 by : European Coordinating Committee for Artificial Intelligence

Download or read book ECAI 2010 written by European Coordinating Committee for Artificial Intelligence and published by IOS Press. This book was released on 2010 with total page 1184 pages. Available in PDF, EPUB and Kindle. Book excerpt: LC copy bound in 2 v.: v. 1, p. 1-509; v. 2, p. [509]-1153.

Robot Learning

Author : Suraiya Jabin
Publisher : BoD – Books on Demand
ISBN 13 : 9533071044
Total Pages : 162 pages
Book Rating : 4.5/5 (33 download)

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Book Synopsis Robot Learning by : Suraiya Jabin

Download or read book Robot Learning written by Suraiya Jabin and published by BoD – Books on Demand. This book was released on 2010-08-12 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot Learning is intended for one term advanced Machine Learning courses taken by students from different computer science research disciplines. This text has all the features of a renowned best selling text. It gives a focused introduction to the primary themes in a Robot learning course and demonstrates the relevance and practicality of various Machine Learning algorithms to a wide variety of real-world applications from evolutionary techniques to reinforcement learning, classification, control, uncertainty and many other important fields. Salient features: - Comprehensive coverage of Evolutionary Techniques, Reinforcement Learning and Uncertainty. - Precise mathematical language used without excessive formalism and abstraction. - Included applications demonstrate the utility of the subject in terms of real-world problems. - A separate chapter on Anticipatory-mechanisms-of-human-sensory-motor-coordination and biped locomotion. - Collection of most recent research on Robot Learning.

Inductive Biases in Machine Learning for Robotics and Control

Author : Michael Lutter
Publisher : Springer Nature
ISBN 13 : 3031378326
Total Pages : 131 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Inductive Biases in Machine Learning for Robotics and Control by : Michael Lutter

Download or read book Inductive Biases in Machine Learning for Robotics and Control written by Michael Lutter and published by Springer Nature. This book was released on 2023-07-31 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: One important robotics problem is “How can one program a robot to perform a task”? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots.

Deep Reinforcement Learning and Its Industrial Use Cases

Author : Shubham Mahajan
Publisher : John Wiley & Sons
ISBN 13 : 1394272561
Total Pages : 421 pages
Book Rating : 4.3/5 (942 download)

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Book Synopsis Deep Reinforcement Learning and Its Industrial Use Cases by : Shubham Mahajan

Download or read book Deep Reinforcement Learning and Its Industrial Use Cases written by Shubham Mahajan and published by John Wiley & Sons. This book was released on 2024-10-01 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premise—enabling machines to learn optimal actions within complex environments through trial and error—has broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. “Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications” is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you’re an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency.

Breakthroughs in Decision Science and Risk Analysis

Author : Louis Anthony Cox, Jr.
Publisher : John Wiley & Sons
ISBN 13 : 1118938895
Total Pages : 328 pages
Book Rating : 4.1/5 (189 download)

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Book Synopsis Breakthroughs in Decision Science and Risk Analysis by : Louis Anthony Cox, Jr.

Download or read book Breakthroughs in Decision Science and Risk Analysis written by Louis Anthony Cox, Jr. and published by John Wiley & Sons. This book was released on 2015-02-23 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover recent powerful advances in the theory, methods, and applications of decision and risk analysis Focusing on modern advances and innovations in the field of decision analysis (DA), Breakthroughs in Decision Science and Risk Analysis presents theories and methods for making, improving, and learning from significant practical decisions. The book explains these new methods and important applications in an accessible and stimulating style for readers from multiple backgrounds, including psychology, economics, statistics, engineering, risk analysis, operations research, and management science. Highlighting topics not conventionally found in DA textbooks, the book illustrates genuine advances in practical decision science, including developments and trends that depart from, or break with, the standard axiomatic DA paradigm in fundamental and useful ways. The book features methods for coping with realistic decision-making challenges such as online adaptive learning algorithms, innovations in robust decision-making, and the use of a variety of models to explain available data and recommend actions. In addition, the book illustrates how these techniques can be applied to dramatically improve risk management decisions. Breakthroughs in Decision Science and Risk Analysis also includes: An emphasis on new approaches rather than only classical and traditional ideas Discussions of how decision and risk analysis can be applied to improve high-stakes policy and management decisions Coverage of the potential value and realism of decision science within applications in financial, health, safety, environmental, business, engineering, and security risk management Innovative methods for deciding what actions to take when decision problems are not completely known or described or when useful probabilities cannot be specified Recent breakthroughs in the psychology and brain science of risky decisions, mathematical foundations and techniques, and integration with learning and pattern recognition methods from computational intelligence Breakthroughs in Decision Science and Risk Analysis is an ideal reference for researchers, consultants, and practitioners in the fields of decision science, operations research, business, management science, engineering, statistics, and mathematics. The book is also an appropriate guide for managers, analysts, and decision and policy makers in the areas of finance, health and safety, environment, business, engineering, and security risk management.

Handbook of Reinforcement Learning and Control

Author : Kyriakos G. Vamvoudakis
Publisher : Springer Nature
ISBN 13 : 3030609901
Total Pages : 833 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Handbook of Reinforcement Learning and Control by : Kyriakos G. Vamvoudakis

Download or read book Handbook of Reinforcement Learning and Control written by Kyriakos G. Vamvoudakis and published by Springer Nature. This book was released on 2021-06-23 with total page 833 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.

Advances in Neural Information Processing Systems 11

Author : Michael S. Kearns
Publisher : MIT Press
ISBN 13 : 9780262112451
Total Pages : 1122 pages
Book Rating : 4.1/5 (124 download)

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Book Synopsis Advances in Neural Information Processing Systems 11 by : Michael S. Kearns

Download or read book Advances in Neural Information Processing Systems 11 written by Michael S. Kearns and published by MIT Press. This book was released on 1999 with total page 1122 pages. Available in PDF, EPUB and Kindle. Book excerpt: The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes computer science, neuroscience, statistics, physics, cognitive science, and many branches of engineering, including signal processing and control theory. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented.

Intelligent Robots and Drones for Precision Agriculture

Author : Sundaravadivazhagan Balasubaramanian
Publisher : Springer Nature
ISBN 13 : 3031511956
Total Pages : 479 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Intelligent Robots and Drones for Precision Agriculture by : Sundaravadivazhagan Balasubaramanian

Download or read book Intelligent Robots and Drones for Precision Agriculture written by Sundaravadivazhagan Balasubaramanian and published by Springer Nature. This book was released on with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt:

A Guided Tour of Artificial Intelligence Research

Author : Pierre Marquis
Publisher : Springer Nature
ISBN 13 : 3030061647
Total Pages : 808 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis A Guided Tour of Artificial Intelligence Research by : Pierre Marquis

Download or read book A Guided Tour of Artificial Intelligence Research written by Pierre Marquis and published by Springer Nature. This book was released on 2020-05-08 with total page 808 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Then the volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, reasoning based on similarity (case-based, interpolative, or analogical), as well as reasoning about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statistical computational learning and reinforcement learning (other machine learning topics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.