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Python Reinforcement Learning

You're reading from   Python Reinforcement Learning Solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow

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Product type Course
Published in Apr 2019
Publisher Packt
ISBN-13 9781838649777
Length 496 pages
Edition 1st Edition
Languages
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Authors (4):
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Yang Wenzhuo Yang Wenzhuo
Author Profile Icon Yang Wenzhuo
Yang Wenzhuo
Sean Saito Sean Saito
Author Profile Icon Sean Saito
Sean Saito
Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
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Toc

Table of Contents (27) Chapters Close

Title Page
About Packt
Contributors
Preface
1. Introduction to Reinforcement Learning FREE CHAPTER 2. Getting Started with OpenAI and TensorFlow 3. The Markov Decision Process and Dynamic Programming 4. Gaming with Monte Carlo Methods 5. Temporal Difference Learning 6. Multi-Armed Bandit Problem 7. Playing Atari Games 8. Atari Games with Deep Q Network 9. Playing Doom with a Deep Recurrent Q Network 10. The Asynchronous Advantage Actor Critic Network 11. Policy Gradients and Optimization 12. Balancing CartPole 13. Simulating Control Tasks 14. Building Virtual Worlds in Minecraft 15. Learning to Play Go 16. Creating a Chatbot 17. Generating a Deep Learning Image Classifier 18. Predicting Future Stock Prices 19. Capstone Project - Car Racing Using DQN 20. Looking Ahead 1. Assessments 2. Other Books You May Enjoy Index

Summary


In conclusion, machine learning can be applied to several industries and can be applied very efficiently in financial markets, as you saw in this chapter. We can combine different models, as we did with reinforcement learning and time series, to produce stronger models that suit our use cases. We discussed the use of reinforcement learning and time series to predict the stock market. We worked with an actor-critic model that determined the best action, based on the state of the stock prices, with the aim of maximizing profits. In the end, we obtained a result that boasted an overall profit and included increasing profits over time, indicating that the agent learned more with each state.

In the next chapter, you will learn about the future areas of work. 

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