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.
Summary
We 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 technology
We 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 book
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.
What’s inside
An introduction to reinforcement learning
DRL agents with human-like behaviors
Applying DRL to complex situations
About the reader
For developers with basic deep learning experience.
About the author
Miguel 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 Contents
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
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