Reinforcement Learning and Optimal Control
Reinforcement Learning, Approximate Dynamic Programming, and Neuro-Dynamic Programming are solution methods for large and complex decision problems.
Reinforcement Learning and Optimal Control
Nº de artículo: 17157855

Reinforcement Learning and Optimal Control

Nº de artículo: 17157855

CRC 62469

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Reinforcement Learning, Approximate Dynamic Programming, and Neuro-Dynamic Programming are solution methods for large and complex decision problems.
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What Stands Out

Comprehensive Framework
Offers an integrated approach combining reinforcement learning with optimal control, addressing both theoretical concepts and practical applications for enhanced understanding and implementation in various contexts.
Real-World Applications
Equips readers with the necessary tools and methodologies to apply reinforcement learning techniques in real-world scenarios, ensuring relevance and usability in contemporary automated systems.
Expert Contributions
Authored by leading experts, this book provides authoritative insights and cutting-edge research, ensuring high-quality content that meets the needs of both academics and practitioners in the field.

Detalles de producto

Get the best deals on Reinforcement Learning and Optimal Control First Edition at Ubuy. Shop now for a wide selection and great prices on Costa Rica. Fast and reliable shipping.
  • Addresses large and complex multistage decision problems
  • Offers solution methods based on approximations for suboptimal policies
  • Explores the intersection between artificial intelligence and optimal control
  • Organizes successful methods with solid theoretical foundations
  • Provides intuitive explanations and numerous examples
  • Suitable for self-study or as a textbook with supporting materials
Publisher Athena Scientific
Publication date July 15, 2019
Edition First Edition
Language English
Print length 388 pages
ISBN-10 1886529396
ISBN-13 978-1886529397
Item Weight 1.48 pounds (670 grams)
Dimensions 10 x 8 x 1.5 inches (25.4 x 20.3 x 3.8 cm)

Who Should Buy?

Suitable For
  • Academic Researchers

    Ideal for researchers focusing on reinforcement learning and control theories to advance their studies and projects.

  • Graduate Students

    Beneficial for graduate students in computer science or engineering seeking to deepen their understanding of optimal control.

  • Industry Professionals

    Useful for professionals in AI or robotics looking to implement reinforcement learning for practical applications.

Not Suitable For
  • Casual Learners

    Not suitable for those seeking basic introductions or quick tutorials on reinforcement learning fundamentals.

DESCRIPCIÓN DEL PRODUCTO

Reinforcement Learning and Optimal Control

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Intelligence & Semantics Editorial Review

The Reinforcement Learning and Optimal Control First Edition by Bertsekas is praised for its technical expertise and mastery of exposition. The book offers clear explanations of the key ideas behind RL and optimal control and the differences between machine learning and traditional adaptive control approaches. The algorithms are thoroughly explained and made transparent. It is recommended for readers with a basic background in dynamic programming or control theory. The book is regarded as a must-read for anyone who wants to understand RL. The author uses a broad range of examples to guide readers starting from exact DP for finite-horizon problems. The book provides an understanding of RL for finite-horizon problems and DP and RL theory for infinite-horizon problems. The author also presents the aggregation method and its special characteristics. The book is good at explaining the theory of finite-state and finite-action problems in a self-contained and rigorous manner. The author reminds readers throughout the book that the potential challenges lie in the implementation of theoretical techniques.

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ventajas

  • Technical expertise and mastery of exposition.
  • Clear explanations of key ideas behind RL and optimal control.
  • Thoroughly explained algorithms.
  • Wide range of examples to guide readers.
  • Provides an understanding of RL for finite-horizon problems and DP and RL theory for infinite-horizon problems.
  • Explains the aggregation method and its special characteristics.
  • Self-contained and rigorous.

Product Price History

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