You're reading from Deep Reinforcement Learning with Python Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

Product type Paperback

Published in Sep 2020

Publisher Packt

ISBN-13 9781839210686

Length 760 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (22) Chapters

Preface

1. Fundamentals of Reinforcement Learning

2. A Guide to the Gym Toolkit FREE CHAPTER

3. The Bellman Equation and Dynamic Programming

4. Monte Carlo Methods

5. Understanding Temporal Difference Learning

6. Case Study – The MAB Problem

7. Deep Learning Foundations

8. A Primer on TensorFlow

9. Deep Q Network and Its Variants

10. Policy Gradient Method

11. Actor-Critic Methods – A2C and A3C

12. Learning DDPG, TD3, and SAC

13. TRPO, PPO, and ACKTR Methods

14. Distributional Reinforcement Learning

15. Imitation Learning and Inverse RL

16. Deep Reinforcement Learning with Stable Baselines

17. Reinforcement Learning Frontiers

18. Other Books You May Enjoy

19. Index

Appendix 1 – Reinforcement Learning Algorithms

1. Appendix 2 – Assessments

TensorFlow 2.0 and Keras

TensorFlow 2.0 has got some really cool features. It sets the eager execution mode by default. It provides a simplified workflow and uses Keras as the main API for building deep learning models. It is also backward compatible with TensorFlow 1.x versions.

To install TensorFlow 2.0, open your Terminal and type the following command:

pip install tensorflow==2.0.0-alpha0

Since TensorFlow 2.0 uses Keras as a high-level API, we will look at how Keras works in the next section.

Bonjour Keras

Keras is another popularly used deep learning library. It was developed by François Chollet at Google. It is well known for its fast prototyping, and it makes model building simple. It is a high-level library, meaning that it does not perform any low-level operations on its own, such as convolution. It uses a backend engine for doing that, such as TensorFlow. The Keras API is available in tf.keras, and TensorFlow 2.0 uses it as the primary API.

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