Texplore Temporal Difference Reinforcement Learning For Robots And Time Constrained Domains

Author : Todd Hester
Publisher : Springer
Page : 165 pages
File Size : 48,6 Mb
Release : 2013-06-22
Category : Technology & Engineering
ISBN : 9783319011684

Get Book

TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains by Todd Hester Pdf

This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agent’s lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples.

Author : Sven Behnke,Manuela M. Veloso,Arnoud Visser,Rong Xiong
Publisher : Springer
Page : 684 pages
File Size : 42,5 Mb
Release : 2014-07-16
Category : Computers
ISBN : 9783662444689

Get Book

RoboCup 2013: Robot World Cup XVII by Sven Behnke,Manuela M. Veloso,Arnoud Visser,Rong Xiong Pdf

This book includes the thoroughly refereed post-conference proceedings of the 17th Annual RoboCup International Symposium, held in Eindhoven, The Netherlands, in June 2013. The 20 revised papers presented together with 11 champion team papers, 3 best paper awards, 11 oral presentations, and 19 special track on open-source hard- and software papers were carefully reviewed and selected from 78 submissions. The papers present current research and educational activities within the fields of robotics and artificial intelligence with a special focus to robot hardware and software, perception and action, robotic cognition and learning, multi-robot systems, human-robot interaction, education and edutainment, and applications.

Author : Judy A. Franklin,Tom M. Mitchell,Sebastian Thrun
Publisher : Springer Science & Business Media
Page : 218 pages
File Size : 48,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461304715

Get Book

Recent Advances in Robot Learning by Judy A. Franklin,Tom M. Mitchell,Sebastian Thrun Pdf

Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).

Author : Richard S. Sutton,Andrew G. Barto
Publisher : MIT Press
Page : 549 pages
File Size : 46,6 Mb
Release : 2018-11-13
Category : Computers
ISBN : 9780262352703

Get Book

Reinforcement Learning, second edition by Richard S. Sutton,Andrew G. Barto Pdf

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.

Author : Alexandros Iosifidis,Anastasios Tefas
Publisher : Academic Press
Page : 638 pages
File Size : 40,8 Mb
Release : 2022-02-04
Category : Computers
ISBN : 9780323885720

Get Book

Deep Learning for Robot Perception and Cognition by Alexandros Iosifidis,Anastasios Tefas Pdf

Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis

Author : Csaba Grossi
Publisher : Springer Nature
Page : 89 pages
File Size : 50,5 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031015519

Get Book

Algorithms for Reinforcement Learning by Csaba Grossi Pdf

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

Author : Peter Edwards,Derek Sleeman
Publisher : Morgan Kaufmann
Page : 497 pages
File Size : 40,7 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9781483298535

Get Book

Machine Learning Proceedings 1992 by Peter Edwards,Derek Sleeman Pdf

Machine Learning Proceedings 1992

Author : Marco Wiering,Martijn van Otterlo
Publisher : Springer Science & Business Media
Page : 638 pages
File Size : 47,5 Mb
Release : 2012-03-05
Category : Technology & Engineering
ISBN : 9783642276453

Get Book

Reinforcement Learning by Marco Wiering,Martijn van Otterlo Pdf

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research. Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge representation in reinforcement learning settings.

Author : Eitan Altman
Publisher : Routledge
Page : 256 pages
File Size : 54,9 Mb
Release : 2021-12-17
Category : Mathematics
ISBN : 9781351458245

Get Book

Constrained Markov Decision Processes by Eitan Altman Pdf

This book provides a unified approach for the study of constrained Markov decision processes with a finite state space and unbounded costs. Unlike the single controller case considered in many other books, the author considers a single controller with several objectives, such as minimizing delays and loss, probabilities, and maximization of throughputs. It is desirable to design a controller that minimizes one cost objective, subject to inequality constraints on other cost objectives. This framework describes dynamic decision problems arising frequently in many engineering fields. A thorough overview of these applications is presented in the introduction. The book is then divided into three sections that build upon each other.

Author : Zhiyuan Sun,Bing Leno da Silva
Publisher : Springer Nature
Page : 187 pages
File Size : 47,6 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015816

Get Book

Lifelong Machine Learning, Second Edition by Zhiyuan Sun,Bing Leno da Silva Pdf

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

Author : Warren B. Powell
Publisher : John Wiley & Sons
Page : 1090 pages
File Size : 55,9 Mb
Release : 2022-03-15
Category : Mathematics
ISBN : 9781119815037

Get Book

Reinforcement Learning and Stochastic Optimization by Warren B. Powell Pdf

REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.

Author : Vincent Francois-Lavet,Peter Henderson,Riashat Islam,Marc G. Bellemare,Joelle Pineau
Publisher : Foundations and Trends (R) in Machine Learning
Page : 156 pages
File Size : 40,7 Mb
Release : 2018-12-20
Category : Electronic
ISBN : 1680835386

Get Book

An Introduction to Deep Reinforcement Learning by Vincent Francois-Lavet,Peter Henderson,Riashat Islam,Marc G. Bellemare,Joelle Pineau Pdf

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.

Author : Anonim
Publisher : Unknown
Page : 876 pages
File Size : 42,5 Mb
Release : 1995
Category : Neural computers
ISBN : UCSD:31822021494810

Get Book

1995 IEEE International Conference on Neural Networks by Anonim Pdf

Author : Leslie Pack Kaelbling
Publisher : MIT Press
Page : 206 pages
File Size : 52,9 Mb
Release : 1993
Category : Computers
ISBN : 0262111748

Get Book

Learning in Embedded Systems by Leslie Pack Kaelbling Pdf

Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics and machine learning. Presenting interesting, new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial and error experience with an external world. The text is a detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behaviour to a complex, changing environment. Such systems include mobile robots, factory process controllers and long-term software databases.

Author : Luca Tonin,Emanuele Menegatti,Damien Coyle
Publisher : Frontiers Media SA
Page : 114 pages
File Size : 44,5 Mb
Release : 2021-04-07
Category : Technology & Engineering
ISBN : 9782889666737

Get Book

Advances in the Integration of Brain-Machine Interfaces and Robotic Devices by Luca Tonin,Emanuele Menegatti,Damien Coyle Pdf