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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
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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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Toc

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

The code outline

Okay, now let's switch to the code, which is in directory Chapter24 in the book's repository. In this section, I'm going to give a quick outline of my implementation and the key design decisions, but before that, I have to emphasize the important points about the code to set up the correct expectations:

  • I'm not a researcher, so the original goal of this code was just to reimplement the paper's method. Unfortunately, the paper has very few details about the exact hyperparameters used, so I had to guess and experiment a lot, and still, my results are very different from those published in the paper.
  • At the same time, I've tried to implement everything in a general way to simplify further experiments. For example, the exact details about the cube state and transformations are abstracted away, which allows us to implement more puzzles similar to the 3×3 cube just by adding a new module. In my code, two cubes are implemented...
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