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 Connect 4 bot

To see the method in action, let's implement AlphaGo Zero for Connect 4. The game is for two players with fields 6×7. Players have disks of two different colors, which they drop in turn into any of the seven columns. The disks fall to the bottom, stacking vertically. The game objective is to be the first to form a horizontal, vertical, or diagonal group of four disks of the same color. Two game situations are shown in the following diagram. In the first situation, the first player has just won, while in the second, the second player is going to form a group.

Figure 23.2: Two game positions in Connect 4

Despite its simplicity, this game has 4.5*10¹² different game states, which is challenging for computers to solve with brute force. This example consists of several tools and library modules:

Chapter23/lib/game.py: A low-level game representation that contains functions to make moves, encode, and decode the game state, and other game-related...