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 the chapter by understanding what imitation learning is and how supervised imitation learning works. Next, we learned about the DAgger algorithm, where we aggregate the dataset obtained over a series of iterations and learn the optimal policy.

After looking at DAgger, we learned about DQfD, where we prefill the replay buffer with expert demonstrations and pre-train the agent with expert demonstrations before the training phase.

Moving on, we learned about IRL. We understood that in reinforcement learning, we try to find the optimal policy given the reward function, but in IRL, we try to learn the reward function given the expert demonstrations. When we have derived the reward function from the expert demonstrations using IRL, we can use the reward function to train our agent to learn the optimal policy using any reinforcement learning algorithm. We then explored how to learn the reward function using the maximum entropy IRL algorithm.

At the end...