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Hands-On Reinforcement Learning with Python

You're reading from Hands-On Reinforcement Learning with Python Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788836524

Length 318 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (16) Chapters

Preface

1. Introduction to Reinforcement Learning

2. Getting Started with OpenAI and TensorFlow FREE CHAPTER

3. The Markov Decision Process and Dynamic Programming

4. Gaming with Monte Carlo Methods

5. Temporal Difference Learning

6. Multi-Armed Bandit Problem

7. Deep Learning Fundamentals

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. Capstone Project – Car Racing Using DQN

13. Recent Advancements and Next Steps

14. Assessments

15. Other Books You May Enjoy

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Training an agent to play Doom

Doom is a very popular first-person shooter game. The goal of the game is to kill monsters. Doom is another example of a partially observable MDP as the agent's (player) view is limited to 90 degrees. The agent has no idea about the rest of the environment. Now, we will see how can we use DRQN to train our agent to play Doom.

Instead of OpenAI Gym, we will use the ViZDoom package to simulate the Doom environment to train our agent. To learn more about the ViZDoom package, check out its official website at http://vizdoom.cs.put.edu.pl/. We can install ViZDoom simply by using the following command:

pip install vizdoom

ViZDoom provides a lot of Doom scenarios and those scenarios can be found in the package folder vizdoom/scenarios.

Basic Doom game...

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Authors (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

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