AI Book Club: Grokking Deep Reinforcement Learning

AI Builders and Learners SF Octobers's book is "Grokking Deep Reinforcement Learning"! ​This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back. ​Feel free to join the discussion even if you have not read the book chapters! :) ​Want to discuss the contents during the reading week? Join the Flyte MLOps Slack group. (https://flyte-org.slack.com/join/shared_invite/zt-47whsaemd-S4KFny5mk0uHCtRF2gU7ig?utm_source=luma#/shared-invite/email) ​------------------------------------------------- About the book: • ​Title: Grokking Deep Reinforcement Learning • ​Authors: Miguel Morales • ​Published: December 2020 ​Manning (Promo code: AIBookClub should give you 45% off: https://www.manning.com/books/grokking-deep-reinforcement-learning (https://www.manning.com/books/grokking-deep-reinforcement-learning?utm_source=luma) ​O'rielly platform: https://learning.oreilly.com/library/view/grokking-deep-reinforcement/9781617295454/ (https://learning.oreilly.com/library/view/grokking-deep-reinforcement/9781617295454/?utm_source=luma) ​ ​Chapters: • ​1 Introduction to deep reinforcement learning • ​2 Mathematical foundations of reinforcement learning • ​3 Balancing immediate and long-term goals • ​4 Balancing the gathering and use of information • ​5 Evaluating agents’ behaviors • ​6 Improving agents’ behaviors • ​7 Achieving goals more effectively and efficiently • ​8 Introduction to value-based deep reinforcement learning • ​9 More stable value-based methods • ​10 Sample-efficient value-based methods • ​11 Policy-gradient and actor-critic methods • ​12 Advanced actor-critic methods • ​13 Toward artificial general intelligence ​Book Description ​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.