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Reinforcement Learning with TensorFlow

You're reading from Reinforcement Learning with TensorFlow A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

Product type Paperback

Published in Apr 2018

Publisher Packt

ISBN-13 9781788835725

Length 334 pages

Edition 1st Edition

Languages

Python

Tools

OpenAI Gym

Concepts

Reinforcement Learning

Author (1):

Dutta

View More author details

Table of Contents (21) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Deep Learning – Architectures and Frameworks FREE CHAPTER

2. Training Reinforcement Learning Agents Using OpenAI Gym

3. Markov Decision Process

4. Policy Gradients

5. Q-Learning and Deep Q-Networks

6. Asynchronous Methods

7. Robo Everything – Real Strategy Gaming

8. AlphaGo – Reinforcement Learning at Its Best

9. Reinforcement Learning in Autonomous Driving

10. Financial Portfolio Management

11. Reinforcement Learning in Robotics

12. Deep Reinforcement Learning in Ad Tech

13. Reinforcement Learning in Image Processing

14. Deep Reinforcement Learning in NLP

1. Further topics in Reinforcement Learning

2. Other Books You May Enjoy

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Index

Summary

In this chapter, we covered the most famous algorithms in reinforcement learning, the policy gradients and actor-critic algorithms. There is a lot of research going on in developing policy gradients to benchmark better results in reinforcement learning. Further study of policy gradients include Trust Region Policy Optimization (TRPO), Natural Policy Gradients, and Deep Dependency Policy Gradients (DDPG), which are beyond the scope of this book.

In the next chapter, we will take a look at the building blocks of Q-Learning, applying deep neural networks, and many more techniques.

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Authors (1)

Dutta

Dutta

Sayon Dutta is an Artificial Intelligence researcher and developer. A graduate from IIT Kharagpur, he owns the software copyright for Mobile Irrigation Scheduler. At present, he is an AI engineer at Wissen Technology. He co-founded an AI startup Marax AI Inc., focused on AI-powered customer churn prediction. With over 2.5 years of experience in AI, he invests most of his time implementing AI research papers for industrial use cases, and weightlifting.

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