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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, 2nd Edition
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About Deep Reinforcement Learning Hands-On: Apply Modern
Review "Reinforcement learning is an exciting field that opens up the path to new possibilities. Max Lapan has written an excellent guide to reinforcement learning that not only explains the concepts but shows us how to use RL in developing our own programs." --Dr. Marco Wiering, Assistant Professor in Artificial Intelligence, University of Groningen"Oh wow! What a book! I have just finished reading the book Deep Reinforcement Learning Hands-On 2nd Edition by Maxim Lapan. What a tome! It is a large book! Extremely well written, instructive, and insightful." --Dr. Tristan Behrens, Founding Member of AI Guild and Independent Deep Learning Hands-On Adviser Product Description 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 moreKey FeaturesSecond edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods Apply 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 models Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others Build a practical hardware robot trained with RL methods for less than $100 Discover Microsoft's TextWorld environment, which is an interactive fiction games platform Use discrete optimization in RL to solve a Rubik's Cube Teach your agent to play Connect 4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI chatbots Discover 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 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 NavigationContinuous Action SpaceRL in RoboticsTrust Regions – PPO, TRPO, ACKTR, and SACBlack-Box Optimization in RLAdvanced explorationBeyond Model-Free – ImaginationAlphaGo ZeroRL in Discrete OptimisationMult
