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 off the chapter by understanding what TD learning is and how it takes advantage of both DP and the MC method. We learned that, just like DP, TD learning bootstraps, and just like the MC method, TD learning is a model-free method.

Later, we learned how to perform a prediction task using TD learning, and then we looked into the algorithm of the TD prediction method.

Going forward, we learned how to use TD learning for a control task. First, we learned about the on-policy TD control method called SARSA, and then we learned about the off-policy TD control method called Q learning. We also learned how to find the optimal policy in the Frozen Lake environment using the SARSA and Q learning methods.

We also learned the difference between SARSA and Q learning methods. We understood that SARSA is an on-policy algorithm, meaning that we use a single epsilon-greedy policy to select an action in the environment and also to compute the Q value of the next state-action...