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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
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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

Deep Learning with PyTorch

In the previous chapter, you became familiar with open source libraries, which provided you with a collection of reinforcement learning (RL) environments. However, recent developments in RL, and especially its combination with deep learning (DL), now make it possible to solve much more challenging problems than ever before. This is partly due to the development of DL methods and tools. This chapter is dedicated to one such tool, PyTorch, which enables us to implement complex DL models with just a bunch of lines of Python code.

The chapter doesn't pretend to be a complete DL manual, as the field is very wide and dynamic; however, we will cover:

  • The PyTorch library specifics and implementation details (assuming that you are already familiar with DL fundamentals)
  • Higher-level libraries on top of PyTorch, with the aim of simplifying common DL problems
  • The library PyTorch ignite, which will be used in some examples

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