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Deep Reinforcement Learning Hands-On. - Second Edition

You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Pages 826 pages
Edition 2nd Edition
Languages
Author (1):
Maxim Lapan Maxim Lapan
Profile icon Maxim Lapan

Table of Contents (28) Chapters

Preface 1. What Is Reinforcement Learning? 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. 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

Adding text descriptions

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

Implementation

I'm 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 Chapter16/wob_click_mm_train.py.) In comparison to our clicker...

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