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Python Reinforcement Learning

You're reading from   Python Reinforcement Learning Solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow

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Product type Course
Published in Apr 2019
Publisher Packt
ISBN-13 9781838649777
Length 496 pages
Edition 1st Edition
Languages
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Authors (4):
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Yang Wenzhuo Yang Wenzhuo
Author Profile Icon Yang Wenzhuo
Yang Wenzhuo
Sean Saito Sean Saito
Author Profile Icon Sean Saito
Sean Saito
Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
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Toc

Table of Contents (27) Chapters Close

Title Page
About Packt
Contributors
Preface
1. Introduction to Reinforcement Learning FREE CHAPTER 2. Getting Started with OpenAI and TensorFlow 3. The Markov Decision Process and Dynamic Programming 4. Gaming with Monte Carlo Methods 5. Temporal Difference Learning 6. Multi-Armed Bandit Problem 7. Playing Atari Games 8. Atari Games with Deep Q Network 9. Playing Doom with a Deep Recurrent Q Network 10. The Asynchronous Advantage Actor Critic Network 11. Policy Gradients and Optimization 12. Balancing CartPole 13. Simulating Control Tasks 14. Building Virtual Worlds in Minecraft 15. Learning to Play Go 16. Creating a Chatbot 17. Generating a Deep Learning Image Classifier 18. Predicting Future Stock Prices 19. Capstone Project - Car Racing Using DQN 20. Looking Ahead 1. Assessments 2. Other Books You May Enjoy Index

Summary


Congratulations! You just learned four important things. The first one is how to implement an Atari game emulator using gym, and how to play Atari games for relaxation and having fun. The second one is that you learned how to preprocess data in reinforcement learning tasks such as Atari games. For practical machine learning applications, you will spend a great deal of time on understanding and refining data, which affects the performance of an AI system a lot. The third one is the deep Q-learning algorithm. You learned the intuition behind it, for example, why the replay memory is necessary, why the target network is needed, where the update rule comes from, and so on. The final one is that you learned how to implement DQN using TensorFlow, and how to visualize the training process. Now, you are ready for the more advanced topics that we will discuss in the following chapters.

In the next chapter, you will learn how to simulate classic control tasks, and how to implement the state...

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