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

Deep Q learning from demonstrations

We learned that in imitation learning, we try to learn from expert demonstrations. Can we make use of expert demonstrations in DQN and perform better? Yes! In this section, we will learn how to make use of expert demonstrations in DQN using an algorithm called DQfD.

In the previous chapters, we have learned about several types of DQN. We started off with vanilla DQN, and then we explored various improvements to the DQN, such as double DQN, dueling DQN, prioritized experience replay, and more. In all these methods, the agent tries to learn from scratch by interacting with the environment. The agent interacts with the environment and stores their interaction experience in a buffer called a replay buffer and learns based on their experience.

In order for the agent to perform better, it has to gather a lot of experience from the environment, add it to the replay buffer, and train itself. However, this method costs us a lot of training...