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

The baseline

In the rest of the chapter, we will take the Atari Pong environment that you are already familiar with and try to speed up its convergence. As a baseline, we will take the same simple DQN that we used in Chapter 8, DQN Extensions, and the hyperparameters will also be the same. To compare the effect of our changes, we will use two characteristics:

The number of frames that we consume from the environment every second (FPS). It indicates how fast we can communicate with the environment during the training. It is very common in RL papers to indicate the number of frames that the agent observed during the training; normal numbers are 25M-50M frames. So, if our FPS=200, it will take days. In such calculations, you need to take into account that RL papers commonly report raw environment frames. But if frame skip is used (and it almost always is), this number needs to be divided by the frame skip factor, which is commonly equal to 4. In our measurements, we calculate...