| Springer Nature B.V.

73,480원  | 20230726  | 9783031283956

This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems.

 | Taylor & Francis

588,060원  | 20250523  | 9780415438698

The proceedings of the 4th International Symposium on Earth Reinforcement. The first volume contains 137 papers selected for the symposium covering almost every aspect of earth reinforcement. The second volume contains texts of the special and keynote lectures.

 |

109,120원  | 20251216  | 9781032651439

This book covers most of the areas of DRL with a special focus on its mathematical and algorithmic foundations. It is a useful guide for undergraduate and early graduate students to the fast-developing areas of DRL and its myriad applications.

 |

267,300원  | 20251216  | 9781032659794

This book covers most of the areas of DRL with a special focus on its mathematical and algorithmic foundations. It is a useful guide for undergraduate and early graduate students to the fast-developing areas of DRL and its myriad applications.

 | Springer

90,930원  | 20240817  | 9783031373473

Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology.

 | Now Publishers

181,910원  | 20250625  | 9781638285762

The ongoing evolution of power systems presents a multifaceted challenge, namely to ensure a safe and reliable operation amidst a dynamic and uncertain environment. This necessitates not only achieving performance objectives, but also adhering to diverse constraints encompassing operational limits, regulatory compliance, and environmental goals. For example, challenges for modern power systems include renewable energy integration, distributed resources, and complex operational requirements.

Sutton, Richard S./ Barto, Andrew G.  | Bradford Book

185,620원  | 19980301  | 9780262193986

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 ...

 | CRC Press

216,040원  | 20250325  | 9781032955957

Reinforcement for Modern Concrete Structures provides readers with the foundational information on various properties and characteristics of the different types of rebar used in concrete structures.

 | Springer Nature B.V.

73,480원  | 20221227  | 9781484288368

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 deep learning networks in reinforcement learning.

Marc G. Bellemare, Will Dabney, Mark Rowland  | MIT Press

65,000원  | 20230801  | 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.

 | Hutson Street Press

56,870원  | 20250522  | 9781025183190

This meticulously reproduced 1908 "Handbook and Catalogue of Concrete Reinforcement" offers a fascinating glimpse into the early 20th-century construction industry. Published by the American Steel & Wire Co., this handbook provides detailed specifications, illustrations, and practical guidance on the use of steel reinforcement in concrete structures.

 | Hutson Street Press

32,980원  | 20250522  | 9781025187051

This meticulously reproduced 1908 "Handbook and Catalogue of Concrete Reinforcement" offers a fascinating glimpse into the early 20th-century construction industry. Published by the American Steel & Wire Co., this handbook provides detailed specifications, illustrations, and practical guidance on the use of steel reinforcement in concrete structures.

 | Springer Nature B.V.

73,480원  | 20230815  | 9783031373466

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.

Sutton, Richard S., Barto, Andrew G.  | Bradford Book

110,000원  | 20181113  | 9780262039246

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

 | Springer Nature B.V.

73,480원  | 20221028  | 9783031090318

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.