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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
89% of respondents would recommend this to a friend
Item #:
52769558
SAR 397
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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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What Stands Out
Practical Applications
Focuses on real-world problems like chatbots and robotics, ensuring learners can apply theoretical knowledge to practical scenarios, improving relevance in today's technology-driven landscape.
Modern Techniques
Covers contemporary reinforcement learning methods, equipping readers with cutting-edge skills that enhance their ability to tackle complex challenges across various domains.
Hands-On Approach
Emphasizes experiential learning through hands-on projects, which cultivates a deeper understanding of deep reinforcement learning concepts while encouraging experimentation and innovation.
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 |
Who Should Buy?
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Aspiring Developers
Those new to AI and machine learning seeking practical knowledge in reinforcement learning techniques for real-world applications.
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Industry Professionals
Professionals looking to enhance their skill set by applying modern reinforcement learning methods in fields like robotics or automation.
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Students and Learners
Students pursuing studies in computer science or AI who want hands-on experience with practical reinforcement learning problems.
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Beginner Programmers
Individuals with no prior programming or AI knowledge may find the content challenging and overwhelming without foundational skills.
Product Description
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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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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