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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 (21) Chapters Close

Preface 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 Other Books You May Enjoy Index

Adding text description

As the last example of this chapter, we'll add text description of the problem into observations of our model. We've already mentioned that some problems contain vital information given in a text description, like the index of tabs needed to be clicked or list of entries that the agent needs to check. The same information is shown on the top of the image observation, but pixels is not always the best representation of a simple text.

To take this text into account, we need to extend our model's input from an image only to an image and text data. We have worked with text in the previous chapter, so a Recurrent Neural Network (RNN) is quite an obvious choice (maybe not the best for such a toy problem but it is flexible and scalable). We are not going to cover this example in detail but will just focus on the most important points of the implementation (the whole code is in Chapter13/wob_click_mm_train.py). In comparison to our clicker model, text...

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