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Item #:
17157855
Reinforcement Learning and Optimal Control
Item #:
17157855
AMD 46568
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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.
Product Details
| 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?
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Academic Researchers
Ideal for researchers focusing on reinforcement learning and control theories to advance their studies and projects.
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Graduate Students
Beneficial for graduate students in computer science or engineering seeking to deepen their understanding of optimal control.
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Industry Professionals
Useful for professionals in AI or robotics looking to implement reinforcement learning for practical applications.
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Casual Learners
Not suitable for those seeking basic introductions or quick tutorials on reinforcement learning fundamentals.
Product Description
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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Pros
- 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.
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