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

Vectorized environments

One of the very interesting and useful features of Stable Baselines is that we can train our agent in multiple independent environments either in separate processes (using SubprocVecEnv) or in the same process (using DummyVecEnv).

For example, say we are training our agent in a cart pole balancing environment – instead of training our agent only in a single cart pole balancing environment, we can train our agent in the multiple cart pole balancing environments.

We generally train our agent in a single environment per step but now we can train our agent in multiple environments per step. This helps our agent to learn more quickly. Now, our state, action, reward, and done will be in the form of a vector since we are training our agent in multiple environments. So, we call this a vectorized environment.

There are two types of vectorized environment offered by Stable Baselines: