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Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788834247
Length 546 pages
Edition 1st 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 (23) Chapters Close

Deep Reinforcement Learning Hands-On
Contributors
Preface
Other Books You May Enjoy
1. What is Reinforcement Learning? FREE CHAPTER 2. OpenAI Gym 3. Deep Learning with PyTorch 4. The Cross-Entropy Method 5. Tabular Learning and the Bellman Equation 6. Deep Q-Networks 7. DQN Extensions 8. Stocks Trading Using RL 9. Policy Gradients – An Alternative 10. The Actor-Critic Method 11. Asynchronous Advantage Actor-Critic 12. Chatbots Training with RL 13. Web Navigation 14. Continuous Action Space 15. Trust Regions – TRPO, PPO, and ACKTR 16. Black-Box Optimization in RL 17. Beyond Model-Free – Imagination 18. AlphaGo Zero Index

Example – GAN on Atari images


Almost every book about DL uses the MNIST dataset to show you the power of DL, which, over the years, has made this dataset extremely boring, like a fruit fly for genetic researchers. To break this tradition, and add a bit more fun to the book, I've tried to avoid well-beaten paths and illustrate PyTorch using something different. You may have heard about generative adversarial networks (GANs), which were invented and popularized by Ian Goodfellow. In this example, we'll train a GAN to generate screenshots of various Atari games.

The simplest GAN architecture is this: we have two networks and the first works as a "cheater" (it is also called generator), and the other is a "detective" (another name is discriminator). Both networks compete with each other: the generator tries to generate fake data, which will be hard for the discriminator to distinguish from your dataset, and the discriminator tries to detect the generated data samples. Over time, both networks...

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