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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

Tweaking wrappers

The final step in our sequence of experiments will be tweaking wrappers applied to the environment. This is very easy to overlook, as wrappers are normally written once, or just borrowed from other code, applied to the environment, and left to sit there. But you should be aware of their importance in terms of the speed and convergence of your method. For example, the normal DeepMind-style stack of wrappers applied to an Atari game looks like this:

  1. NoopResetEnv: applies a random amount of NOOP operations to the game reset. In some Atari games, this is needed to remove weird initial observations.
  2. MaxAndSkipEnv: applies max to N observations (four by default) and returns this as an observation for the step. This solves the "flickering" problem in some Atari games, when the game draws different portions of the screen on even and odd frames (a normal practice among 2600 developers to increase the complexity of the game's sprites).
  3. EpisodicLifeEnv...
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