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Deep Reinforcement Learning Hands-On

You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788834247

Length 546 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

View More author details

Table of Contents (21) Chapters

Preface

1. What is Reinforcement Learning? FREE CHAPTER

2. OpenAI Gym

3. Deep Learning with PyTorch

4. The Cross-Entropy Method

5. Tabular Learning and the Bellman Equation

6. Deep Q-Networks

7. DQN Extensions

8. Stocks Trading Using RL

9. Policy Gradients – An Alternative

10. The Actor-Critic Method

11. Asynchronous Advantage Actor-Critic

12. Chatbots Training with RL

13. Web Navigation

14. Continuous Action Space

15. Trust Regions – TRPO, PPO, and ACKTR

16. Black-Box Optimization in RL

17. Beyond Model-Free – Imagination

18. AlphaGo Zero

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Index

Connect4 bot

To see the method in action, let's implement AlphaGo Zero for Connect4. The game is for two players with fields 6 × 7. Players have disks of two different colors, which they drop in turn to any of the seven columns. 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 diagram. On the first, the red player has just won, while on the second, the blue player is going to form a group.

Connect4 bot

Figure 2: Two game positions in Connect4

Despite the 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:

Chapter18/lib/game.py: Low-level game representation, which contains functions to make moves, encode and decode the game state, and other game-related utilities.
Chapter18/lib/mcts.py: MCTS implementation...

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Authors (1)

Maxim Lapan

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.

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