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

Generative adversarial imitation learning

Generative Adversarial Imitation Learning (GAIL) is another very popular IRL algorithm. As the name suggests, it is based on Generative Adversarial Networks (GANs), which we learned about in Chapter 7, Deep Learning Foundations. To understand how GAIL works, we should first recap how GANs work.

In a GAN, we have two networks: one is the generator and the other is the discriminator. The role of the generator is to generate new data points by learning the distribution of the input dataset. The role of the discriminator is to classify whether a given data point is generated by the generator (learned distribution) or whether it is from the real data distribution.

Minimizing the loss function of a GAN implies minimizing the Jensen Shannon (JS) divergence between the real data distribution and the fake data distribution (learned distribution). The JS divergence is used to measure how two probability distributions differ from each other...