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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

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788834247
Length 546 pages
Edition 1st 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 (21) Chapters Close

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 Other Books You May Enjoy Index

The AlphaGo Zero method

Overview

At a high level, the method consists of three components, all of which will be explained in detail later, so don't worry if something is not completely clear from this section:

  • We traverse constantly the game tree, using the Monte-Carlo Tree Search (MCTS) algorithm, the core idea of which is to semi-randomly walk down the game states, expanding them and gathering statistics about the frequency of moves and underlying game outcomes. As the game tree is huge, both in terms of the depth and width, we're not trying to build the full tree, just randomly sampling the most promising paths of it (that's the source of the method's name).
  • At every moment, we have a best player, which is the model used to generate the data via the self-play. Initially, this model has random weights, so it makes the moves randomly, like a four-year-old just learning how chess pieces move. However, over time, we'll replace this best player with better variations...
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