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Deep Reinforcement Learning Hands-On

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

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Length 826 pages
Edition 2nd Edition
Languages
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Table of Contents (28) Chapters Close

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

Board games

Most board games provide a setup that is different from an arcade scenario. The Atari game suite assumes that one player is making decisions in some environment with complex dynamics. By generalizing and learning from the outcome of their actions, the player improves their skills, increasing their final score. In a board game setup, however, the rules of the game are usually quite simple and compact. What makes the game complicated is the number of different positions on the board and the presence of an opponent with an unknown strategy who tries to win the game.

With board games, the ability to observe the game state and the presence of explicit rules opens up the possibility of analyzing the current position, which isn't the case for Atari. This analysis means taking the current state of the game, evaluating all the possible moves that we can make, and then choosing the best move as our action.

The simplest approach to evaluation is to iterate over the possible...

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