Download or read book TEXPLORE Temporal Difference Reinforcement Learning for Robots and Time Constrained Domains written by Todd Hester and published by Springer. This book was released on 2013-06-22 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Reinforcement Learning written by Marco Wiering and published by Springer Science & Business Media. This book was released on 2012-03-05 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Efficient Reinforcement Learning Using Gaussian Processes written by Marc Peter Deisenroth and published by KIT Scientific Publishing. This book was released on 2010 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.

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.

Download or read book Reinforcement Learning and Dynamic Programming Using Function Approximators written by Lucian Busoniu and published by CRC Press. This book was released on 2017-07-28 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.

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

Download or read book Deep Reinforcement Learning written by Robert Johnson and published by HiTeX Press. This book was released on 2024-10-27 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Deep Reinforcement Learning: An Essential Guide" offers a comprehensive introduction to one of the most dynamic and transformative areas of artificial intelligence. This book meticulously unravels the intricate concepts of deep reinforcement learning (DRL), bridging foundational theories with cutting-edge applications. Addressing both newcomers and experienced practitioners, it provides a structured exploration from the basics of neural networks and reinforcement learning to the sophisticated mechanisms that drive core algorithms like DQN, PPO, and policy gradient methods. The book emphasizes real-world applications, showcasing DRL's impact across gaming, finance, healthcare, and autonomous systems, illustrating its vast potential and versatility. By understanding the strategic balance of exploration and exploitation, readers gain insight into designing intelligent agents capable of thriving in complex environments. As DRL continues to evolve, the text also delves into current challenges and future directions, such as ethical considerations, safety, and efficiency, preparing readers to contribute to and innovate within this rapidly advancing field. Comprehensive yet accessible, this guide is an invaluable resource for anyone aspiring to harness the power of deep reinforcement learning.

Download or read book Computers and Games written by H. Jaap van den Herik and published by Springer. This book was released on 2007-09-28 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 5th International Conference on Computers and Games, CG 2006, co-located with the 14th World Computer-Chess Championship and the 11th Computer Olympiad. The 24 revised papers cover all aspects of artificial intelligence in computer-game playing. Topics addressed are evaluation and learning, search, combinatorial games and theory opening and endgame databases, single-agent search and planning, and computer Go.

Download or read book Advanced Intelligent Computing Technology and Applications written by De-Shuang Huang and published by Springer Nature. This book was released on with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.

Download or read book Recent Advances in Reinforcement Learning written by Sertan Girgin and published by Springer Science & Business Media. This book was released on 2008-12 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised and selected papers of the 8th European Workshop on Reinforcement Learning, EWRL 2008, which took place in Villeneuve d'Ascq, France, during June 30 - July 3, 2008. The 21 papers presented were carefully reviewed and selected from 61 submissions. They are dedicated to the field of and current researches in reinforcement learning.

Download or read book Efficient Transportation and Pavement Systems Characterization Mechanisms Simulation and Modeling written by Imad L. Al-Qadi and published by CRC Press. This book was released on 2008-11-01 with total page 924 pages. Available in PDF, EPUB and Kindle. Book excerpt: Internationally, significant attention is given to transport sustainability including planning, design, construction, evaluation, safety and durability of the road system. The 4th International Gulf Conference on Roads: Efficient Transportation and Pavement Systems - Characterization, Mechanisms, Simulation, and Modeling, hosted by the University o

Download or read book Artificial Neural Networks and Machine Learning ICANN 2023 written by Lazaros Iliadis and published by Springer Nature. This book was released on 2023-09-21 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023. The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.

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.

Download or read book Agent and Multi Agent Systems Technologies and Applications written by Anne Hakansson and published by Springer. This book was released on 2009-05-30 with total page 884 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the Third KES Symposium on Agent and Multi-agent Systems – Technologies and Applications (KES-AMSTA 2009)––held at Uppsala University in Sweden during June 3-5, 2009. The symposium was organized by Uppsala University, KES International and its Focus Group on Agent and Multi-agent Systems. The KES-AMSTA Symposium series is a sub-series of the KES Conference series. Following the successes of the First KES Symposium on Agent and Multi-agent Systems – Technologies and Applications (KES-AMSTA 2007), held in Wroclaw, Poland, from May 31 to 1 June 2007––and the Second KES Symposium on Agent and Multi-agent Systems – Technologies and Applications (KES-AMSTA 2008) held in Incheon, Korea, March 26-28, 2008––KES-AMSTA 2009 featured keynote talks, oral and poster presentations, and a number of workshops and invited sessions, closely aligned to the themes of the conference. The aim of the symposium was to provide an international forum for scientific - search into the technologies and applications of agent and multi-agent systems. Agent and multi-agent systems are an innovative type of modern software system and have long been recognized as a promising technology for constructing autonomous, c- plex and intelligent systems. A key development in the field of agent and multi-agent systems has been the specification of agent communication languages and formali- tion of ontologies. Agent communication languages are intended to provide standard declarative mechanisms for agents to communicate knowledge and make requests of each other, whereas ontologies are intended for conceptualization of the knowledge domain.

Download or read book From Animals to Animats 2 written by Jean-Arcady Meyer and published by MIT Press. This book was released on 1993 with total page 1018 pages. Available in PDF, EPUB and Kindle. Book excerpt: More than sixty contributions in From Animals to Animats 2 byresearchers in ethology, ecology, cybernetics, artificial intelligence, robotics, and related fieldsinvestigate behaviors and the underlying mechanisms that allow animals and, potentially, robots toadapt and survive in uncertain environments. Jean-Arcady Meyer is Director of Research, CNRS, Paris.Herbert L. Roitblat is Professor of Psychology at the University of Hawaii at Manoa. Stewart W.Wilson is a scientist at The Rowland Institute for Science, Cambridge,Massachusetts. Topics covered: The Animat Approach to Adaptive Behavior,Perception and Motor Control, Action Selection and Behavioral Sequences, Cognitive Maps and InternalWorld Models, Learning, Evolution, Collective Behavior.

Download or read book Encyclopedia of Machine Learning written by Claude Sammut and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 1061 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.