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...
Apress Media, LLC., 2023. — 435 p. — ISBN-13: 978-1-4842-8835-1. 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...
Apress Media, LLC., 2023. — 435 p. — ISBN-13: 978-1-4842-8835-1. 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...
Apress Media, LLC., 2023. — 435 p. — ISBN-13: 978-1-4842-8835-1. 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...
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 of...
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 of...
Arizona State University, 2022. - 180 p. The purpose of the notes is to provide an entry point to reinforcement learning , largely 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 making a choice for a research-oriented term paper. They roughly cover the...
The MIT Press, 2023. — 379 p. — (Adaptive Computation and Machine Learning). 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...
Cambridge (Mass.): The MIT Press, 2023. — 384 p. — ISBN 9780262048019. 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...
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,...
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...
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 Apply...
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 Apply...
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 -...
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 -...
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...
Packt Publishing, 2019. — 362 p. — ISBN: 1789616719, 9781789616712. Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym Key Features Explore the design principles of reinforcement learning and deep reinforcement learning models Use dynamic programming to solve design issues...
Packt Publishing, 2019. — 362 p. — ISBN: 1789616719, 9781789616712. Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym Key Features Explore the design principles of reinforcement learning and deep reinforcement learning models Use dynamic programming to solve design issues...
Packt Publishing, 2019. — 362 p. — ISBN: 1789616719, 9781789616712. Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym Key Features Explore the design principles of reinforcement learning and deep reinforcement learning models Use dynamic programming to solve design issues...
Packt Publishing, 2019. — 362 p. — ISBN: 1789616719, 9781789616712. Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym Key Features Explore the design principles of reinforcement learning and deep reinforcement learning models Use dynamic programming to solve design issues...
Packt Publishing, 2019. — 362 p. — ISBN: 1789616719, 9781789616712. Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym Key Features Explore the design principles of reinforcement learning and deep reinforcement learning models Use dynamic programming to solve design issues...
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...
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...
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, 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...
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 past...
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—such as Go, Atari games, and DotA 2—to robotics.
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 remarkable...
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 remarkable...
London: BPB Online, 2022. — 446 p. — ISBN 978-93-55512-055. Практическое глубокое обучение с подкреплением на Python 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...
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,...
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,...
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...
O’Reilly Media, Inc., 2025. — 236 р. — 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...
O’Reilly Media, Inc., 2025. — 236 р. — 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...
O’Reilly Media, Inc., 2025. — 236 р. — 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...
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...
Packt Publishing, 2018. — 546 p. — ISBN: 978-1788834247. This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO,...
Packt Publishing, 2018. — 546 p. — ISBN: 978-1788834247. !Code files only This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy...
Packt Publishing, 2018. — 546 p. — ISBN: 978-1788834247. This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO,...
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...
Birmingham: Packt Publishing, 2019. — 355 p. — ISBN: 1789131111. Key Features Learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks. Understand and develop model-free and model-based algorithms for building self-learning agents . Work with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution...
Packt Publishing, 2019. — 356 p. — ISBN: 978-1-78913-111-6. Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will...
Packt Publishing, 2019. — 356 p. — ISBN: 978-1-78913-111-6. Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will...
Packt Publishing, 2019. — 356 p. — ISBN: 978-1-78913-111-6. Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will...
Packt Publishing, 2019. — 356 p. — ISBN: 978-1-78913-111-6. Code files only! Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing...
Springer, 2022. — 194 p. — ISBN 3031090292. In ancient games such as chess or Go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With...
John Wiley & Sons, Inc., 2025. — 416 p. — ISBN 978-1-394-27255-6. Глубокое обучение с подкреплением и примеры его промышленного использования: искусственный интеллект для реальных приложений 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,...
John Wiley & Sons, Inc., 2025. — 416 p. — ISBN 978-1-394-27255-6. Глубокое обучение с подкреплением и примеры его промышленного использования: искусственный интеллект для реальных приложений 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,...
John Wiley & Sons, Inc., 2025. — 416 p. — ISBN 978-1-394-27255-6. Глубокое обучение с подкреплением и примеры его промышленного использования: искусственный интеллект для реальных приложений 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,...
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...
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...
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...
Springer, 2009. — 206 p. — ISBN: 978-3540891864, e-ISBN: 978-3540891871.
Motivated learning is an emerging research field in artificial intelligence and cognitive modelling. 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 achieve...
Cambridge: Cambridge University Press, 2022. — 453 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...
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...
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...
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...
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...
Manning Publications, 2020. — 505 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...
Manning Publications, 2020. — 505 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...
Manning Publications, 2020. — 505 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...
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...
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, 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,...
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...
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...
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...
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...
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...
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 OpenAI...
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....
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...
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 book...
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 book...
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 learning...
Packt Publishing, 2018. — 453 p. — ISBN: 978-1-78899-161-2. Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries 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,...
Packt Publishing, 2018. — 453 p. — ISBN: 978-1-78899-161-2. Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries 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,...
Packt Publishing, 2018. — 287 p. — ISBN: 978-1-78899-161-2. Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries 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,...
Packt Publishing, 2018. — 287 p. — ISBN: 978-1-78899-161-2. Code files only! Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries 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...
2nd Edition. — Apress Media LLC, 2024. — 659 p. — ISBN-13: 979-8-8688-0272-0. Gain a theoretical understanding to 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...
2nd Edition. — Apress Media LLC, 2024. — 659 p. — ISBN-13: 979-8-8688-0273-7. Gain a theoretical understanding to 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...
2nd Edition. — Apress Media LLC, 2024. — 659 p. — ISBN-13: 979-8-8688-0273-7. Gain a theoretical understanding to 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...
2nd Edition. — Apress Media LLC, 2024. — 659 p. — ISBN-13: 979-8-8688-0273-7. Gain a theoretical understanding to 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...
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, 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...
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
N.-Y: Chapman and Hall/CRC, 2015. - 573p.
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...
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...
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...
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...
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...
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.
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...
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, 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...
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...
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...
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...
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...
O’Reilly, 2020. — 408 p. — ISBN: 1098114833. 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...
O’Reilly Media, Inc., 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...
O’Reilly Media, Inc., 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...
Independent publication, 2024. — 224 р. 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....
Independent publication, 2024. — 224 р. 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....
Independent publication, 2024. — 224 р. 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...
Springer, 2024. — 573 p. — ISBN 9811949328. Reinforcement Learning: Theory and Python Implementation is a tutorial book on reinforcement learning, with explanations of both theory and applications. Starting from a uniform mathematical framework, this book derives the theory of modern reinforcement learning systematically and introduces all mainstream reinforcement learning...
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 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...
Manning Publications, 2020. — 367 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...
Manning Publications, 2020. — 367 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...
Manning Publications, 2020. — 367 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...
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...
М.: ДМК Пресс, 2020. — 286 с. Эта книга поможет читателю овладеть алгоритмами обучения с подкреплением (ОП) и научиться реализовывать их при создании самообучающихся агентов. В первой части рассматриваются различные элементы ОП, сфера его применения, инструменты, необходимые для работы в среде ОП. Вторая и третья части посвящены непосредственно алгоритмам. В числе прочего автор...
Пер. с англ. Волошко Р. — СПб.: Питер, 2023. — 464 с.: ил. — (Библиотека программиста). — ISBN: 978-5-4461-3944-6. Мы учимся, взаимодействуя с окружающей средой, и получаемые вознаграждения и наказания определяют наше поведение в будущем. Глубокое обучение с подкреплением привносит этот естественный процесс в искусственный интеллект и предполагает анализ результатов для...
Пер. с англ. Волошко Р. — СПб.: Питер, 2023. — 464 с.: ил. — (Библиотека программиста). — ISBN: 978-5-4461-3944-6. Мы учимся, взаимодействуя с окружающей средой, и получаемые вознаграждения и наказания определяют наше поведение в будущем. Глубокое обучение с подкреплением привносит этот естественный процесс в искусственный интеллект и предполагает анализ результатов для...
Пер. с англ. Волошко Р. — СПб.: Питер, 2023. — 464 с.: ил. — (Библиотека программиста). — ISBN: 978-5-4461-3944-6. Мы учимся, взаимодействуя с окружающей средой, и получаемые вознаграждения и наказания определяют наше поведение в будущем. Глубокое обучение с подкреплением привносит этот естественный процесс в искусственный интеллект и предполагает анализ результатов для...
СПб.: Питер, 2020. — 320 с. Глубокое обучение с подкреплением (Reinforcement Learning) - самое популярное и перспективное направление искусственного интеллекта. Практическое изучение RL на Python поможет освоить не только базовые, но и передовые алгоритмы глубокого обучения с подкреплением. Вы начнете с основных принципов обучения с подкреплением, OpenAI Gym и TensorFlow,...
Пер. с англ. — 2-е изд. (эл.). — М.: Бином. Лаборатория знаний, 2014. — 402 с. — (Адаптивные и интеллектуальные системы). — ISBN: 978-5-9963-2500-9. Обучение с подкреплением является одной из наиболее активно развивающихся областей, связанных с созданием искусственных интеллектуальных систем. Оно основано на том, что агент пытается максимизировать получаемый выигрыш, действуя в...
Reinforcement Learning Авторы: Ричард С. Саттон, Эндрю Г. Барто Издательство: Бином. Лаборатория знаний Серия: Адаптивные и интеллектуальные системы ISBN: 978-5-94774-351-7; 2012 г. Переводчики Е. Романов, Ю. Тюменцев Язык Русский Страниц 400 стр.
БХВ, 2023. — 400 c. Книга посвящена промышленно-ориентированному применению обучения с подкреплением (Reinforcement Learning, RL). Объяснено, как обучать промышленные и научные системы решению любых пошаговых задач методом проб и ошибок – без подготовки узкоспециализированных учебных множеств данных и без риска переобучить или переусложнить алгоритм. Рассмотрены марковские...
Пер. с англ. Екатерины Черских. — СПб.: БХВ-Петербург, 2023. — 400 с.: ил. — ISBN 978-5-9775-6885-2. Книга посвящена промышленно-ориентированному применению обучения с подкреплением (Reinforcement Learning, RL). Объяснено, как обучать промышленные и научные системы решению любых пошаговых задач методом проб и ошибок – без подготовки узкоспециализированных учебных множеств...
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