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 Stable Baselines is and how to install it. Then, we learned to create our first agent with Stable Baselines using a DQN. We also learned how to save and load an agent. Next, we learned how to create multiple independent environments using vectorization. We also learned about two types of vectorized environment called SubprocVecEnv and DummyVecEnv.

We learned that in SubprocVecEnv, we run each environment in a different process, whereas in DummyVecEnv, we run each environment in the same process.

Later, we learned how to implement a DQN and its variants to play Atari games using Stable Baselines. Next, we learned how to implement A2C and also how to create a custom policy network. Moving on, we learned how to implement DDPG and also how to view the computational graph in TensorBoard.

Going further, we learned how to set up the MuJoCo environment and how to train an agent to walk using TRPO. We also learned how to record...