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Author : Aviv Tamar
Publisher :
ISBN 13 :
Total Pages : 134 pages
Book Rating : 4.:/5 (951 download)
Download or read book Risk-sensitive and Efficient Reinforcement Learning Algorithms written by Aviv Tamar and published by . This book was released on 2015 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Prashanth L. A.
Publisher :
ISBN 13 : 9781638280262
Total Pages : 170 pages
Book Rating : 4.2/5 (82 download)
Download or read book Risk-Sensitive Reinforcement Learning Via Policy Gradient Search written by Prashanth L. A. and published by . This book was released on 2022-06-15 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) is one of the foundational pillars of artificial intelligence and machine learning. An important consideration in any optimization or control problem is the notion of risk, but its incorporation into RL has been a fairly recent development. This monograph surveys research on risk-sensitive RL that uses policy gradient search.The authors survey some of the recent work in this area specifically where policy gradient search is the solution approach. In the first risk-sensitive RL setting, they cover popular risk measures based on variance, conditional value at-risk and chance constraints, and present a template for policy gradient-based risk-sensitive RL algorithms using a Lagrangian formulation. For the setting where risk is incorporated directly into the objective function, they consider an exponential utility formulation, cumulative prospect theory, and coherent risk measures.Written for novices and experts alike the authors have made the text completely self-contained but also organized in a manner that allows expert readers to skip background chapters. This is a complete guide for students and researchers working on this aspect of machine learning.
Author : Csaba Szepesvari
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1608454924
Total Pages : 89 pages
Book Rating : 4.6/5 (84 download)
Download or read book Algorithms for Reinforcement Learning written by Csaba Szepesvari and published by Morgan & Claypool Publishers. This book was released on 2010 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.
Author : Peter Geibel
Publisher :
ISBN 13 : 9783832253226
Total Pages : 218 pages
Book Rating : 4.2/5 (532 download)
Download or read book Risk Sensitive Approaches for Reinforcement Learning written by Peter Geibel and published by . This book was released on 2006 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Marc G. Bellemare
Publisher : MIT Press
ISBN 13 : 0262048019
Total Pages : 385 pages
Book Rating : 4.2/5 (62 download)
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.
Author : Tengyu Xu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Towards the Understanding of Sample Efficient Reinforcement Learning Algorithms written by Tengyu Xu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL), which aims at designing a suitable policy for an agent via interacting with an unknown environment, has achieved remarkable success in the recent past. Despite its great potential to solve complex tasks, current RL algorithms suffer from requiring a large amount of interaction data, which could result in significant cost in real world applications. Thus, the goal of this thesis is to study the sample complexity of fundamental RL algorithms, and then, to propose new RL algorithms to solve real-world problems with provable efficiency. To achieve this goal, this thesis makes the contributions along the following three main directions: 1. For policy evaluation, we proposed a new on-policy algorithm called variance reduce TD (VRTD) and established the state-of-the-art sample complexity result for off-policy two-timescale TD learning algorithms. 2. For policy optimization, we established improved sample complexity bounds for on-policy actor-critic (AC) type algorithms and proposed the first doubly robust off-policy AC algorithm with provable efficiency guarantee. 3. We proposed three new algorithms: GenTD, CRPO and PARTED to address challenging practical problems of general value function evaluation, safe RL and trajectory-wise reward RL, respectively, with provable efficiency.
Author : Kyriakos G. Vamvoudakis
Publisher : Springer Nature
ISBN 13 : 3030609901
Total Pages : 833 pages
Book Rating : 4.0/5 (36 download)
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.
Author : Zheng Wen
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (878 download)
Download or read book Efficient Reinforcement Learning with Value Function Generalization written by Zheng Wen and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) is concerned with how an agent should learn to make decisions over time while interacting with an environment. A growing body of work has produced RL algorithms with sample and computational efficiency guarantees. However, most of this work focuses on "tabula rasa" learning; i.e. algorithms aim to learn with little or no prior knowledge about the environment. Such algorithms exhibit sample complexities that grow at least linearly in the number of states, and they are of limited practical import since state spaces in most relevant contexts are enormous. There is a need for algorithms that generalize in order to learn how to make effective decisions at states beyond the scope of past experience. This dissertation focuses on the open issue of developing efficient RL algorithms that leverage value function generalization (VFG). It consists of two parts. In the first part, we present sample complexity results for two classes of RL problems -- deterministic systems with general forms of VFG and Markov decision processes (MDPs) with a finite hypothesis class. The results provide upper bounds that are independent of state and action space cardinalities and polynomial in other problem parameters. In the second part, building on insights from our sample complexity analyses, we propose randomized least-square value iteration (RLSVI), a RL algorithm for MDPs with VFG via linear hypothesis classes. The algorithm is based on a new notion of randomized value function exploration. We compare through computational studies the performance of RLSVI against least-square value iterations (LSVI) with Boltzmann exploration or epsilon-greedy exploration, which are widely used in RL with VFG. Results demonstrate that RLSVI is orders of magnitude more efficient.
Author : Boris Belousov
Publisher : Springer Nature
ISBN 13 : 3030411885
Total Pages : 197 pages
Book Rating : 4.0/5 (34 download)
Download or read book Reinforcement Learning Algorithms: Analysis and Applications written by Boris Belousov and published by Springer Nature. This book was released on 2021-01-02 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universität Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.
Author : Peter Whittle
Publisher :
ISBN 13 :
Total Pages : 266 pages
Book Rating : 4.F/5 ( download)
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.
Author : Archie Addo
Publisher : Business Expert Press
ISBN 13 : 1949443523
Total Pages : 127 pages
Book Rating : 4.9/5 (494 download)
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 127 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.
Author : Milad Farsi
Publisher : John Wiley & Sons
ISBN 13 : 111980857X
Total Pages : 276 pages
Book Rating : 4.1/5 (198 download)
Download or read book Model-Based Reinforcement Learning written by Milad Farsi and published by John Wiley & Sons. This book was released on 2023-01-05 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal control and dynamic programming – or on algorithms—most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework—from design to application—of a more tractable model-based reinforcement learning technique. Model-Based Reinforcement Learning readers will also find: A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.
Author : Marc Rigter
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)
Download or read book Risk-sensitive and Robust Model-based Reinforcement Learning and Planning written by Marc Rigter and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Richard S. Sutton
Publisher : Springer Science & Business Media
ISBN 13 : 1461536189
Total Pages : 173 pages
Book Rating : 4.4/5 (615 download)
Download or read book Reinforcement Learning written by Richard S. Sutton and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.
Author : Nicholas Roy
Publisher : MIT Press
ISBN 13 : 0262315734
Total Pages : 501 pages
Book Rating : 4.2/5 (623 download)
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.
Author : Dongruo Zhou
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)
Download or read book Efficient Reinforcement Learning Through Uncertainties written by Dongruo Zhou and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is centered around the concept of uncertainty-aware reinforcement learning (RL), which seeks to enhance the efficiency of RL by incorporating uncertainty. RL is a vital mathematical framework in the field of artificial intelligence (AI) for creating autonomous agents that can learn optimal behaviors through interaction with their environments. However, RL is often criticized for being sample inefficient and computationally demanding. To tackle these challenges, the primary goals of this dissertation are twofold: to offer theoretical understanding of uncertainty-aware RL and to develop practical algorithms that utilize uncertainty to enhance the efficiency of RL. Our first objective is to develop an RL approach that is efficient in terms of sample usage for Markov Decision Processes (MDPs) with large state and action spaces. We present an uncertainty-aware RL algorithm that incorporates function approximation. We provide theoretical proof that this algorithm achieves near minimax optimal statistical complexity when learning the optimal policy. In our second objective, we address two specific scenarios: the batch learning setting and the rare policy switch setting. For both settings, we propose uncertainty-aware RL algorithms with limited adaptivity. These algorithms significantly reduce the number of policy switches compared to previous baseline algorithms while maintaining a similar level of statistical complexity. Lastly, we focus on estimating uncertainties in neural network-based estimation models. We introduce a gradient-based method that effectively computes these uncertainties. Our approach is computationally efficient, and the resulting uncertainty estimates are both valid and reliable. The methods and techniques presented in this dissertation contribute to the advancement of our understanding regarding the fundamental limits of RL. These research findings pave the way for further exploration and development in the field of decision-making algorithm design.
Author : Leslie Pack Kaelbling
Publisher : Springer
ISBN 13 : 0585336563
Total Pages : 286 pages
Book Rating : 4.5/5 (853 download)
Download or read book Recent Advances in Reinforcement Learning written by Leslie Pack Kaelbling and published by Springer. This book was released on 2007-08-28 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
