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

Implementing GAIL

In this section, let's explore how to implement Generative Adversarial Imitation Learning (GAIL) with Stable Baselines. In Chapter 15, Imitation Learning and Inverse RL, we learned that we use the generator to generate the state-action pair in a way that the discriminator is not able to distinguish whether the state-action pair is generated using the expert policy or the agent policy. We train the generator to generate a policy similar to an expert policy using TRPO, while the discriminator is a classifier and it is optimized using Adam.

To implement GAIL, we need expert trajectories so that our generator learns to mimic the expert trajectory. Okay, so how can we obtain the expert trajectory? First, we use the TD3 algorithm to generate expert trajectories and then create an expert dataset. Then, using this expert dataset, we train our GAIL agent. Note that instead of using TD3, we can also use any other algorithm for generating expert trajectories.

...