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Reinforcement Learning with TensorFlow

You're reading from   Reinforcement Learning with TensorFlow A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788835725
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Sayon Dutta Sayon Dutta
Author Profile Icon Sayon Dutta
Sayon Dutta
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Table of Contents (17) Chapters Close

Preface 1. Deep Learning – Architectures and Frameworks 2. Training Reinforcement Learning Agents Using OpenAI Gym FREE CHAPTER 3. Markov Decision Process 4. Policy Gradients 5. Q-Learning and Deep Q-Networks 6. Asynchronous Methods 7. Robo Everything – Real Strategy Gaming 8. AlphaGo – Reinforcement Learning at Its Best 9. Reinforcement Learning in Autonomous Driving 10. Financial Portfolio Management 11. Reinforcement Learning in Robotics 12. Deep Reinforcement Learning in Ad Tech 13. Reinforcement Learning in Image Processing 14. Deep Reinforcement Learning in NLP 15. Further topics in Reinforcement Learning 16. Other Books You May Enjoy

Summary


In this chapter, we looked at one of the recently published approaches to using deep reinforcement learning in financial portfolio management. We looked at a problem statement in financial portfolio management, the objectives of a portfolio manager, and mapped the problem statement to a reinforcement learning task. We also learned about different financial metrics for benchmarking performance and different existing online portfolio management approaches. This research topic of automating financial portfolio using deep reinforcement learning is among the most challenging tasks to solve in the AI community. Therefore, apart from the approach covered in this chapter do try to study other traditional machine learning approaches in algorithmic trading.

In the next chapter, we will study the use of reinforcement learning in robotics, the current challenges, and their proposed solutions.

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