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

Summary

We started off the chapter by understanding biological and artificial neurons. Then we learned about ANNs and their layers. We learned different types of activation functions and how they are used to introduce nonlinearity in the network.

Later, we learned about the forward and backward propagation in the neural network. Next, we learned how to implement an ANN. Moving on, we learned about RNNs and how they differ from feedforward networks. Next, we learned about the variant of the RNN called LSTM. Going forward, we learned about CNNs, how they use different types of layers, and the architecture of CNNs in detail.

At the end of the chapter, we learned about an interesting algorithm called GAN. We understood the generator and discriminator component of GAN and we also explored the architecture of GAN in detail. Followed by that, we examined the loss function of GAN in detail.

In the next chapter, we will learn about one of the most popularly used deep...