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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

The first training objective

Let's now discuss what we want our robot to do and how we're going to get there. It's not very hard to notice that the potential capabilities of the hardware described are quite limited:

  • We have only four servos with a constrained angle of rotation: This makes our robot's movements highly dependent on friction with the surface, as it can't bring its individual legs up, which is also the case with the Minitaur robot, which has two motors attached to every leg.
  • Our hardware capacity is small: The memory is limited, the central processing unit (CPU) is not very fast, and no hardware accelerators are present. In the subsequent sections, we will take a look at how to deal with those limitations to some extent.
  • We have no external connectivity besides a micro-USB port: Some boards might have Wi-Fi hardware, which could be used to offload the NN inference to a larger machine, but in this chapter's example, I'm...
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