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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

Chapter 9. Playing Doom with a Deep Recurrent Q Network

In the last chapter, we saw how to build an agent using a Deep Q Network (DQN) in order to play Atari games. We have taken advantage of neural networks for approximating the Q function, used the convolutional neural network (CNN) to understand the input game screen, and taken the past four game screens to better understand the current game state. In this chapter, we will learn how to improve the performance of our DQN by taking advantage of the recurrent neural network (RNN). We will also look at what is partially observable with the Markov Decision Process (MDP) and how we can solve that using a Deep Recurrent Q Network (DRQN). Following this, we will learn how to build an agent to play the game Doom using a DRQN. Finally, we will see a variant of DRQN called Deep Attention Recurrent Q Network (DARQN), which augments the attention mechanism to the DRQN architecture. 

In this chapter, you will learn the following topics:

  • DRQN
  • Partially...
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