You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838826994

Length 826 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Chatbots

Author (1):

Maxim Lapan

View More author details

Table of Contents (28) Chapters

Preface

1. What Is Reinforcement Learning?

2. OpenAI Gym FREE CHAPTER

3. Deep Learning with PyTorch

4. The Cross-Entropy Method

5. Tabular Learning and the Bellman Equation

6. Deep Q-Networks

7. Higher-Level RL Libraries

8. DQN Extensions

9. Ways to Speed up RL

10. Stocks Trading Using RL

11. Policy Gradients – an Alternative

12. The Actor-Critic Method

13. Asynchronous Advantage Actor-Critic

14. Training Chatbots with RL

15. The TextWorld Environment

16. Web Navigation

17. Continuous Action Space

18. RL in Robotics

19. Trust Regions – PPO, TRPO, ACKTR, and SAC

20. Black-Box Optimization in RL

21. Advanced Exploration

22. Beyond Model-Free – Imagination

23. AlphaGo Zero

24. RL in Discrete Optimization

25. Multi-agent RL

26. Other Books You May Enjoy

27. Index

CartPole variance

To check this theoretical conclusion in practice, let's plot our policy gradient variance during the training for both the baseline version and the version without the baseline. The complete example is in Chapter12/01_cartpole_pg.py, and most of the code is the same as in Chapter 11, Policy Gradients – an Alternative. The differences in this version are the following:

It now accepts the command-line option --baseline, which enables the mean subtraction from the reward. By default, no baseline is used.
On every training loop, we gather the gradients from the policy loss and use this data to calculate the variance.

To gather only the gradients from the policy loss and exclude the gradients from the entropy bonus added for exploration, we need to calculate the gradients in two stages. Luckily, PyTorch allows this to be done easily. In the following code, only the relevant part of the training loop is included to illustrate the idea:

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