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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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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

RL's reputation

The perception of deep RL is that it is a tool to be used mostly for game playing. This is not surprising given the fact that, historically, the first success in the field was achieved on the Atari game suite by DeepMind in 2015 (https://deepmind.com/research/dqn/). The Atari benchmark suite (https://github.com/mgbellemare/Arcade-Learning-Environment) turned out to be very successful for RL problems and, even now, lots of research papers use it to demonstrate the efficiency of their methods. As the RL field progresses, the classic 53 Atari games continue to become less and less challenging (at the time of writing, almost all the games have been solved with superhuman accuracy) and researchers are turning to more complex games, like StarCraft and Dota 2.

This perception, which is especially prevalent in the media, is something that I've tried to counterbalance in this book by accompanying Atari games with examples from other domains, including stock trading...

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