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Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3)
85% of respondents would recommend this to a friend
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The first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology
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What Stands Out
Rincian Produk
- First textbook to fully explain the neuro-dynamic programming/reinforcement learning methodology
- Utilizes neural network approximations to overcome dimensionality and modeling challenges
- Enables systems to learn through simulation and improve performance through iterative reinforcement
- Presents a systematic analysis of neuro-dynamic programming algorithms
- Guides successful application through case studies from complex problem areas
- Comprehensive presentation of the science and art behind this methodology
| Publisher | Athena Scientific |
| Publication date | May 1, 1996 |
| Edition | 1st |
| Language | English |
| Print length | 512 pages |
| ISBN-10 | 1886529108 |
| ISBN-13 | 978-1886529106 |
| Item Weight | 1.85 pounds (840 grams) |
| Dimensions | 6.25 x 1 x 9.25 inches (15.9 x 2.5 x 23.5 cm) |
Who Should Buy?
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Graduate Students
Students pursuing advanced studies in optimization and neural networks will find this book essential for their research.
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Researchers
Researchers in operational research and machine learning can utilize the methodologies presented for algorithm development.
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Practitioners
Professionals working in AI and optimization may benefit from the applied techniques discussed for real-world problem-solving.
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Beginner Learners
Individuals lacking background in neural networks and optimization may find the content too advanced and technical.
DESKRIPSI PRODUK
Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3)
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Mathematics Editorial Review
The Neuro-Dynamic Programming Optimization and Neural Computation Series, 3 1st Edition is a highly recommended book for those interested in reinforcement learning and mathematical foundations. The book is well-written and provides a thorough understanding of the concepts of reinforcement learning and approximate dynamic programming. The printing is new and up-to-date, making it a valuable resource for readers. However, it should be noted that this is an advanced book, so prior knowledge or experience in reinforcement learning is recommended.
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Pro
- Well-written and thorough
- New and up-to-date printing
- Valuable resource for those interested in reinforcement learning
Kontra
- Advanced level content
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USD 90
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Fitur & Manfaat
- Explains the neuro-dynamic programming/reinforcement learning methodology
- Breakthrough in the practical application of neural networks and dynamic programming
- Overcomes the curse of dimensionality and modeling
- Allows systems to learn and improve performance through simulation
- Comprehensive analysis of neuro-dynamic programming algorithms
- Successful application through case studies
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