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

Policy gradient methods on Pong

As we covered in the previous section, the vanilla policy gradient method works well on a simple CartPole environment, but it works surprisingly badly on more complicated environments.

For the relatively simple Atari game Pong, our DQN was able to completely solve it in 1M frames and showed positive reward dynamics in just 100k frames, whereas the policy gradient method failed to converge. Due to the instability of policy gradient training, it became very hard to find good hyperparameters and was still very sensitive to initialization.

This doesn't mean that the policy gradient method is bad, because, as you will see in the next chapter, just one tweak of the network architecture to get a better baseline in the gradients will turn the policy gradient method into one of the best methods (the asynchronous advantage actor-critic method). Of course, there is a good chance that my hyperparameters are completely wrong or the code has some hidden...