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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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Concepts
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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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Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
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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

Dataset exploration

It's always a good idea to look at your dataset from various angles, like counting statistics, plotting various characteristics of data, or just eyeballing your data to get a better understanding of your problem and potential issues. The tool cor_reader.py supports the minimalistic functionality for data analysis. By running it with the --show-genres option, you will get all genres from the dataset with a number of movies in each, sorted by the count of movies in order of decreasing size. The top 10 of them are shown as follows:

$ ./cor_reader.py --show-genres
Genres:
drama: 320
thriller: 269
action: 168
comedy: 162
crime: 147
romance: 132
sci-fi: 120
adventure: 116
mystery: 102
horror: 99

The --show-dials option displays dialogues from the movies without any preprocessing, in the order they appear in the database. The number of dialogues is large, so it's worth passing the -g option to filter by genre...

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Authors (1)

Profile icon Maxim Lapan
Maxim Lapan
LinkedIn icon Github icon
Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
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