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 learning what deep Q networks are and how they are used to approximate the Q value. We learned that in a DQN, we use a buffer called the replay buffer to store the agent's experience. Then, we randomly sample a minibatch of experience from the replay buffer and train the network by minimizing the MSE. Moving on, we looked at the algorithm of DQN in more detail, and then we learned how to implement DQN to play Atari games.

Following this, we learned that the DQN overestimates the target value due to the max operator. So, we used double DQN, where we have two Q functions in our target value computation. One Q function parameterized by the main network parameter is used for action selection, and the other Q function parameterized by the target network parameter is used for Q value computation.