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 ContentsIntroduction to deep reinforcement learningMathematical foundations of reinforcement learningBalancing immediate and long-term goalsBalancing the gathering and use of informationEvaluating agents’ behaviorsImproving agents’ behaviorsAchieving goals more effectively and efficientlyIntroduction to value-based deep reinforcement learningMore stable value-based methodsSample-efficient value-based methodsPolicy-gradient and actor-critic methodsAdvanced actor-critic methodsToward artificial general intelligence