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

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To make the training fast, I intentionally chose the hyperparameters of the training process to be small. For example, at every step of the self-play process, only 10 MCTSes were performed, each with a mini-batch size of eight. This, in combination with efficient mini-batch MCTS and the fast game engine, made training very fast.

Basically, after just one hour of training and 2,500 games played in the self-play mode, the produced model was sophisticated enough to be enjoyable to play against. Of course, the level of its play was well below even a kid's level, but it showed some rudimentary strategies and made mistakes in only every other move, which was good progress.

The training was left running for a day, which resulted in 60k games played by a best model and, in total, 105 best model rotations. The training dynamics are shown in the following charts. Figure 23.3 shows the win ratio (win/loss for the current evaluated policy versus the current best policy...