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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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Item #:
52769558
AMD 42752
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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
- Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and moreKey FeaturesSecond edition of the bestselling introduction to deep reinforcement learning, expanded with six new chaptersLearn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methodsApply RL methods to cheap hardware robotics platformsBook DescriptionDeep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.What you will learnUnderstand the deep learning context of RL and implement complex deep learning modelsEvaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and othersBuild a practical hardware robot trained with RL methods for less than $100Discover Microsoft s TextWorld environment, which is an interactive fiction games platformUse discrete optimization in RL to solve a Rubik s CubeTeach your agent to play Connect 4 using AlphaGo ZeroExplore the very latest deep RL research on topics including AI chatbotsDiscover advanced exploration techniques, including noisy networks and network distillation techniquesWho this book is forSome fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RLTable of ContentsWhat Is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman Equation Deep Q-NetworksHigher-Level RL librariesDQN ExtensionsWays to Speed up RLStocks Trading Using RLPolicy Gradients The Actor-Critic MethodAsynchronous Advantage Actor-CriticTraining Chatbots with RLThe TextWorld environmentWeb NavigationContinuous Action SpaceRL in RoboticsTrust Regions Black-Box Optimization in RLAdvanced explorationBeyond Model-Free AlphaGo ZeroRL in Discrete OptimisationMulti-agent RL
| 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
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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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