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Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
New edition of the bestselling guide to deep reinforcement learning and how it’s used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more
Key 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 RL
Table of ContentsWhat Is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksHigher-Level RL librariesDQN ExtensionsWays to Speed up RLStocks Trading Using RLPolicy Gradients – an AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticTraining Chatbots with RLThe TextWorld environmentWeb Navigation<
Key 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 RL
Table of ContentsWhat Is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksHigher-Level RL librariesDQN ExtensionsWays to Speed up RLStocks Trading Using RLPolicy Gradients – an AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticTraining Chatbots with RLThe TextWorld environmentWeb Navigation<
- GenresArtificial Intelligence
828 pages, Kindle Edition
Published January 31, 2020
69 people are currently reading
43 people want to read
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Displaying 1 - 2 of 2 reviews
June 2, 2021
This is the best book, and best resource, I have found about Deep RL. What sets it apart is that it actually has code, and not just abstract ideas and math, which shows that the author really understands much of the topics being explained. However, I can't say it is a five stars because it was very hard to follow at times, code is almost always not clear (you have to decipher it, not just read it), and many times hacks to work around python and framework limitations are used. I know this might be ok for many, since it is a book about Deep RL, not programming, so the tricks he uses might be part of the trade.
However, overall this is a really recommended book for Deep RL for all levels.
However, overall this is a really recommended book for Deep RL for all levels.
June 7, 2021
Wow this surprise me that this book has been rate too low. i think this one really good if you wanna do things but not that good if you wanna learn theory in depth
but this one can build your knowledge to the "Know enough to know what it's going on level", there are a lot of example is easy to grab, if you new to RL and wanna to things you can go for it!
if you wanna know more depth on what going on "grokking deep reinforcement learning", if you wanna know theory math thing go for "an introduction to reinforcement learning"
but this one can build your knowledge to the "Know enough to know what it's going on level", there are a lot of example is easy to grab, if you new to RL and wanna to things you can go for it!
if you wanna know more depth on what going on "grokking deep reinforcement learning", if you wanna know theory math thing go for "an introduction to reinforcement learning"
Displaying 1 - 2 of 2 reviews