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

Variables, constants, and placeholders

Variables, constants, and placeholders are fundamental elements of TensorFlow. However, there is always confusion between these three. Let's look at each element, one by one, and learn the difference between them.

Variables

Variables are containers used to store values. Variables are used as input to several other operations in a computational graph. A variable can be created using the tf.Variable() function, as shown in the following code:

x = tf.Variable(13)

Let's create a variable called W, using tf.Variable(), as follows:

W = tf.Variable(tf.random_normal([500, 111], stddev=0.35), name="weights")

As you can see in the preceding code, we create a variable, W, by randomly drawing values from a normal distribution with a standard deviation of 0.35.

What is that name parameter in tf.Variable()?

It is used to set the name of the variable in the computational graph. So, in the preceding code...