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52769558
Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and
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52769558
UZS 1355529
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Deep Reinforcement Learning Hands-On is an updated and expanded version of the bestselling guide to the latest reinforcement learning tools and techniques.
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Product Details
- New edition of the bestselling guide to deep reinforcement learning with expanded content
- Revised and expanded to include multi-agent methods, discrete optimization, and RL in robotics
- Covers advanced exploration techniques and practical applications of RL including chatbots, robotics, and web automation
- Includes six new chapters on up-to-the-minute developments in RL
- Provides actionable insights into deep Q-networks, policy gradient methods, and continuous control problems
- Assumes fluency in Python and offers an introduction to deep RL with no background in RL required
| Publisher | Packt Publishing |
| Publication date | 31 Jan. 2020 |
| Edition | 2nd Revised edition |
| Language | English |
| Print length | 826 pages |
| ISBN-10 | 1838826998 |
| ISBN-13 | 978-1838826994 |
| Item weight | 1.51 kg |
| Dimensions | 19.05 x 4.75 x 23.5 cm |
Product Description
Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
About This Item
Experience the power of deep reinforcement learning with the second edition of "Deep Reinforcement Learning Hands-On." This comprehensive guide allows you to apply modern RL methods to practical problems across a range of industries, including chatbots, robotics, discrete optimization, web automation, and more. With this book, you'll dive into the world of deep reinforcement learning and gain a deep understanding of its concepts and techniques. You'll explore topics such as value-based methods, policy-based methods, and model-based methods to develop robust and efficient RL models. Whether you're interested in creating sophisticated chatbots, optimizing complex systems, or automating web processes, this book provides you with the knowledge and tools you need. It offers hands-on exercises and real-world examples to help you apply what you've learned and build practical solutions. The large print edition ensures that you can easily read and follow along with the content, making it accessible for readers of all skill levels.
The book is also up-to-date, featuring the latest advancements and techniques in deep reinforcement learning. Don't miss out on the opportunity to master deep reinforcement learning and revolutionize your approach to problem-solving. Get your copy of "Deep Reinforcement Learning Hands-On" today and take your skills to the next level.
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AI & Machine Learning Editorial Review
Deep Reinforcement Learning Hands-On Apply Modern RL Methods To Practical Problems Of Chatbots Robotics Discrete Optimization Web Automation And More offers a comprehensive exploration of reinforcement learning techniques, tailored for practical applications. With 826 pages of clear, structured content, it bridges theoretical knowledge and implementation, showcasing the latest research and practical use cases in areas such as discrete optimization and multi-agent environments. Readers appreciate the accessible language and engaging illustrations, making it a recommended resource for both newcomers and seasoned practitioners in machine learning. The code provided is designed for Python and Pytorch, enhancing its utility for hands-on experimentation.
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Pros
- Comprehensive coverage of RL techniques and applications
- Clear explanations suitable for various skill levels
- Includes practical implementations and code examples
- Engaging illustrations for better understanding
- Structured lessons build upon each other
Cons
- Diagrams are in black and white, which may detract for some
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UZS 1355529
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Features & Benefits
- Learn the fundamentals of RL and implement complex deep learning models
- Discover advanced exploration techniques like network distillation methods and build a practical hardware robot
- Explore deep RL and its application to real-world problems such as cheap hardware robotics platforms
- Evaluate RL methods such as cross-entropy, DQN, actor-critic, and others
- Understand machine learning concept of RL and solve complex problems including discrete optimization and web automation
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