Apress, 2018. — 174 p. Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement...
InTech, 2011, -434 p. Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. Two...
2nd ed. — Cambridge (Mass.): The MIT Press, 2018. — 547 p. — (Adaptive Computation and Machine Learning series). — ISBN: 0262039249. 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...
Springer, 2012. — 652 p. — ISBN: 978-3642276446, e-ISBN: 978-3642276453. Series: Adaptation, Learning, and Optimization (Book 12). 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...
The MIT Press, 1998. - 322 p. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple...
Packt Publishing, 2017. — 336 p. Master different reinforcement learning techniques and their practical implementation using OpenAI Gym, Python and Java Reinforcement learning (RL) is becoming a popular tool for constructing autonomous systems that can improve themselves with experience. We will break the RL framework into its core building blocks, and provide you with details...
N.-Y: Chapman and Hall/CRC, 2015. - 573 p. Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and game players have been successfully explored in recent years. Providing an accessible introduction to the field, this book covers...
InTech, 2011, -482 p. Reinforcement Learning (RL) is oft en referred to as a branch of artificial intelligence and has been one of the central topics in a broad range of scientific fields for the last two decades. Understanding of RL is expected to provide a systematic understanding of adaptive behaviors, including simple classical and operant conditioning of animals as well as...
Birmingham: Packt Publishing, 2018. — 308 p. — ISBN: 1788836529. A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python. Key Features Enter the world of artificial intelligence using the power of Python. An example-rich guide to master various RL and DRL algorithms. Explore various state-of-the-art architectures along with math....
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning. — Morgan and Claypool Publishers, 2010. — 89 p. ISBN: 978-1608454921, e-ISBN: 978-1608454938. 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...
Series: Automation and Control Engineering (Book 39). — CRC Press, 2010. — 275 p. ISBN: 1439821089, 978-1439821084. 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...
Springer, 2019. — 214 p. — ISBN: 9811382840. This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book...
Addison-Wesley Professional, 2019. — 655 p. — (Addison-Wesley Data & Analytics Series). — ISBN: 013517238. Rough Cuts (Work in Progress) ! The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice. Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential...
Second edition. — MIT Press, 2017. — 445 p. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear...
The MIT Press, 2012. — 334 p. This textbook provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Familiarity with elementary concepts of probability is required. Note - This is a draft of the second edition, a work in progress.
Apress, 2019. — 172 p. — ISBN: 1484251261. Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym . Applied Reinforcement Learning with Python introduces you to the theory behind...
New York: Apress, 2019. — 177 p. Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement...
MIT Press, 1998. — 342 p. We first came to focus on what is now known as reinforcement learning in late 1979. We were both at the University of Massachusetts, working on one of the earliest projects to revive the idea that networks of neuronlike adaptive elements might prove to be a promising approach to artificial adaptive intelligence, The project explored the "heterostatic...
Addison-Wesley Professional, 2020. — 817 p. — Addison-Wesley Professional, 2019. — 655 p. — Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games —...
Packt, 2018. — 318 p. Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by...
Apress, 2018. — 174 p. Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement...
Packt Publishing, 2017. — 336 p. Master different reinforcement learning techniques and their practical implementation using OpenAI Gym, Python and Java Reinforcement learning (RL) is becoming a popular tool for constructing autonomous systems that can improve themselves with experience. We will break the RL framework into its core building blocks, and provide you with details...
Springer, 2009. — 206 p. — ISBN: 978-3540891864, e-ISBN: 978-3540891871. Motivated learning is an emerging research field in artificial intelligence and cognitive modeling. Computational models of motivation extend reinforcement learning to adaptive, multitask learning in complex, dynamic environments – the goal being to understand how machines can develop new skills and...
Packt, 2018. — 296 p. Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement...
Logan Styles, 2017. — 58 p. Inside this Book You’ll Discover: The elements of reinforcement learning Reiniforcement Learning vs. other learning types Simulated evironments and Policies A guide to Markov Decision Processes Dynamic Programming An exploration of Monte Carlo Methods The secrets to Q Learning
Manning Publications, 2020. — 472 p. — ISBN: 978-1617295454. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the...
CRC Press, 2023. — 522 p. — ISBN: 978-1-032-12412-4. Reinforcement Learning (RL) is emerging as a practical, powerful technique for solving a variety of complex business problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Although RL is classified as a branch of Machine Learning (ML), it tends to be viewed and treated quite differently from...
Springer, 2019. — 214 p. — ISBN: 9811382840. This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book...
Springer, 2009. — 236 p. In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior...
Springer, 2020. — 237 p. — (Studies in Systems, Decision and Control 265). — ISBN: 978-3-030-33383-6. This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with...
Packt Publishing, 2021. — 484 p. — ISBN: 1838644148, 9781838644147. Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices. Key Features Understand how large-scale state-of-the-art RL algorithms and approaches work....
BPB Online, 2022. — 526 p. — ISBN: 978-93-55512-055. Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow Key Features Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical. Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects. Everything is concise,...
O’Reilly Media, 2024. — 210 p. — ISBN: 109816914X. Reinforcement learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research. This...
2nd Edition. — Apress Media LLC, 2024. — 659 p. — ISBN-13: 979-8-8688-0272-0. Gain a theoretical understanding of the most popular libraries in deep reinforcement learning (deep RL). This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field. New agent environments...
Apress Media, LLC., 2023. — 435 p. — ISBN-13: 978-1-4842-8834-4. This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN – two kinds of neural networks used as...
Springer, 2023. — 484 p. Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex...
Cambridge: Cambridge University Press, 2022. — 453 p. A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in...
The MIT Press, 2023. — 379 p. — (Adaptive Computation and Machine Learning). The first comprehensive guide to Distributional Reinforcement Learning provides 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...
Addison-Wesley Professional, 2019. — 416 p. — ISBN: 978-0135172384. The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved...
London: BPB Online, 2022. — 446 p. — ISBN: 978-93-55512-055. Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow. Key Features Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical. Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects. Everything is...
Wiley-IEEE Press, 2025. — 276 p. — ISBN: 1394206453. A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity. In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing, a team of distinguished researchers delivers an incisive and practical discussion of...
Packt, 2018. — 318 p. — ISBN13: 978-1-78883-652-4. A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the...
Packt, 2018. — 318 p. — ISBN13: 978-1-78883-652-4. A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the...
Springer, 2023. — 96 p. — ISBN: 3031373448. Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization. This...
O’Reilly, 2021. — 408 p. — ISBN: 978-1-098-11483-1. Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI...
Cambridge (Mass.): The MIT Press, 2023. — 384 p. — ISBN: 9780262048019. The first comprehensive guide to distributional reinforcement learning provides 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...
Springer, 2020. — 541 p. — ISBN: 9811540942. Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in...
Addison-Wesley Professional, 2020. — 413 p. — (Addison-Wesley Data & Analytics Series). — ISBN: 013517238. The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the...
O’Reilly Media, Inc., 2025. — 236 p. — ISBN-13: 978-1-098-16914-5. Reinforcement Learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to...
Packt, 2018. — 318 p. — ISBN13: 978-1-78883-652-4. A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the...
Wiley-IEEE Press, 2022. — 289 p. — ISBN: 9781119782742. A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of the novel, model-free, and reinforcement learning...
Manning Publications, 2020. — 351 p. — ISBN: 978-1617295430. Deep Reinforcement Learning in Action teaches you how to program agents that learn and improve based on direct feedback from their environment. You’ll build networks with the popular PyTorch deep learning framework to explore reinforcement learning algorithms ranging from Deep Q-Networks to Policy Gradients methods to...
Arizona State University, 2022. - 180 p. The purpose of the notes is to provide an entry point to reinforcement learning, mainly from a decision, control, and optimization point of view. They have limited scope, but they provide enough background for starting to read literature in the field and for choosing a research-oriented term paper. They roughly cover the material of the...
Springer, 2021. — 214 p. — ISBN: 978-3-030-41187-9. 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,...
Addison-Wesley Professional, 2019. — 416 p. — ISBN: 978-0135172384. The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved...
Wiley, 2023. — 288 p. The book covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking. Covering new advanced models of DRL, e.g., deep dueling...
Springer Vieweg, 2021. — 139 p. — ISBN: 978-3-658-33033-0. Changsheng Hua proposes two approaches, an input/output recovery approach and a performance index-based approach for robustness and performance optimization of feedback control systems. For their data-driven implementation in deterministic and stochastic systems, the author develops Q-learning and natural actor-critic...
John Wiley & Sons, Inc., 2025. — 416 p. — ISBN: 978-1-394-27255-6. 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...
Apress, 2020. — 564 p. — ISBN: 1484265025. This book starts with an introduction to state-based reinforcement learning algorithms involving Markov models, Bellman equations, and writing custom C# code with the aim of contrasting value and policy-based functions in reinforcement learning. Then, you will move on to path finding and navigation meshes in Unity, setting up the ML...
Cambridge University Press, 2022. — 450 p. — ISBN: 9781009051873. A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a...
Manning Publications, 2020. — 398 p. — ISBN: 978-1617295454. Grokking Deep Reinforcement Learning is a beautifully balanced approach to teaching, offering numerous large and small examples, annotated diagrams and code, engaging exercises, and skillfully crafted writing. You'll explore, discover, and learn as you lock in the ins and outs of reinforcement learning, neural...
Packt Publishing, 2021. — 472 p. — ISBN 9781838982546. Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning. Key Features Develop and deploy deep reinforcement learning-based solutions to production pipelines, products, and services. Explore popular reinforcement learning algorithms such as Q-learning,...
Packt Publishing, 2021. — 472 p. — ISBN: 9781838982546. Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning. Key Features Develop and deploy deep reinforcement learning-based solutions to production pipelines, products, and services. Explore popular reinforcement learning algorithms such as Q-learning,...
CRC Press, 2023. — 522 p. — ISBN: 978-1-003-22919-3. Reinforcement Learning (RL) is emerging as a practical, powerful technique for solving a variety of complex business problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Although RL is classified as a branch of Machine Learning (ML), it tends to be viewed and treated quite differently from...
Springer, 2023. — 96 p. — ISBN: 3031373448. Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization. This...
Springer, 2021. — 839 p. — ISBN: 978-3-030-60989-4. 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...
Independent publication, 2024. — 224 p. In a world where machines are constantly pushing the boundaries of human capabilities, one field stands out as a beacon of innovation: Reinforcement Learning (RL). In "Reinforcement Learning: Amplifying AI - Shattering Boundaries with Reinforcement Learning," embark on an exhilarating journey into the heart of AI's next frontier....
Springer, 2023. — 155 p. This book demonstrates that the reliable and secure communication performance of maritime communications can be significantly improved by using intelligent reflecting surface (IRS) aided communication, privacy-aware Internet of Things (IoT) communications, intelligent resource management, and location privacy protection. In the IRS-aided maritime...
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