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

Introducing TensorBoard

TensorBoard is TensorFlow's visualization tool, which can be used to visualize a computational graph. It can also be used to plot various quantitative metrics and the results of several intermediate calculations. When we are training a really deep neural network, it becomes confusing when we have to debug the network. So, if we can visualize the computational graph in TensorBoard, we can easily understand such complex models, debug them, and optimize them. TensorBoard also supports sharing.

As shown in Figure 8.2, the TensorBoard panel consists of several tabs—SCALARS, IMAGES, AUDIO, GRAPHS, DISTRIBUTIONS, HISTOGRAMS, and EMBEDDINGS:

Figure 8.2: TensorBoard

The tabs are pretty self-explanatory. The SCALARS tab shows useful information about the scalar variables we use in our program. For example, it shows how the value of a scalar variable called loss changes over several iterations.

The GRAPHS tab shows the computational graph...