Grokking Deep Reinforcement Learning

Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.SummaryWe all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.About the technologyWe learn by interacting with our environment, and the rewards or punishments we experience guide our future behavior. Deep reinforcement learning brings that same natural process to artificial intelligence, analyzing results to uncover the most efficient ways forward. DRL agents can improve marketing campaigns, predict stock performance, and beat grand masters in Go and chess.About the bookGrokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.What's insideAn introduction to reinforcement learningDRL agents with human-like behaviorsApplying DRL to complex situationsAbout the readerFor developers with basic deep learning experience.About the authorMiguel Morales works on reinforcement learning at Lockheed Martin and is an instructor for the Georgia Institute of Technology’s Reinforcement Learning and Decision Making course.Table of Contents1 Introduction to deep reinforcement learning2 Mathematical foundations of reinforcement learning3 Balancing immediate and long-term goals4 Balancing the gathering and use of information5 Evaluating agents’ behaviors6 Improving agents’ behaviors7 Achieving goals more effectively and efficiently8 Introduction to value-based deep reinforcement learning9 More stable value-based methods10 Sample-efficient value-based methods11 Policy-gradient and actor-critic methods12 Advanced actor-critic methods13 Toward artificial general intelligence

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