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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

Atari experiments

The MountainCar environment is a nice and fast way to experiment with exploration methods, but to conclude the chapter, I've included Atari versions of the DQN and PPO methods with the exploration tweaks we described. As the primary environment, I've used Seaquest, which is a game where the submarine needs to shoot fish and enemy submarines, and save aquanauts. This game is not as famous as Montezuma's Revenge, but it still might be considered as medium-hard exploration, because to continue the game, you need to control the level of oxygen. When it becomes low, the submarine needs to rise to the surface for some time. Without this, the episode will end after 560 steps and with a maximum reward of 20. But once the agent learns how to replenish the oxygen, the game might continue almost infinitely and bring to the agent a 10k-100k score. Surprisingly, traditional exploration methods struggle with discovering this; normally, training gets stuck at 560...

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