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

Tuning hyperparameters

In the previous section, we had Pong solved in three hours of optimization and 9 million frames. Now is a good time to tweak our hyperparameters to speed up convergence. The golden rule here is to tweak one option at a time and make conclusions carefully, as the whole process is stochastic.

In this section, we will start with the original hyperparameters and perform the following experiments:

Increase the learning rate
Increase the entropy beta
Change the count of environments that we are using to gather experience
Tweak the size of the batch

Strictly speaking, the following experiments weren't proper hyperparameter tuning but just an attempt to get a better understanding of how A2C convergence dynamics depend on the parameters. To find the best set of parameters, the full grid search or random sampling of values could give much better results, but they would require much more time and resources.