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Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781835882702
Length 716 pages
Edition 3rd Edition
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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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Toc

Table of Contents (29) Chapters Close

Preface 1. Part 1 Introduction to RL FREE CHAPTER
2. What Is Reinforcement Learning? 3. OpenAI Gym API and Gymnasium 4. Deep Learning with PyTorch 5. The Cross-Entropy Method 6. Part 2 Value-based methods
7. Tabular Learning and the Bellman Equation 8. Deep Q-Networks 9. Higher-Level RL Libraries 10. DQN Extensions 11. Ways to Speed Up RL 12. Stocks Trading Using RL 13. Part 3 Policy-based methods
14. Policy Gradients 15. Actor-Critic Method: A2C and A3C 16. The TextWorld Environment 17. Web Navigation 18. Part 4 Advanced RL
19. Continous Action Space 20. Trust Region Methods 21. Black-Box Optimizations in RL 22. Advanced Exploration 23. Reinforcement Learning with Human Feedback 24. AlphaGo Zero and MuZero 25. RL in Discrete Optimization 26. Multi-Agent RL 27. Bibliography
28. Index

Things to try

As already mentioned, financial markets are large and complicated. The methods that we’ve tried are just the very beginning. Using RL to create a complete and profitable trading strategy is a large project, which can take several months of dedicated labor. However, there are things that we can try to get a better understanding of the topic:

  • Our data representation is definitely not perfect. We don’t take into account significant price levels (support and resistance), round price values, and other financial markets information. Incorporating them into the observation could be a challenging problem, which you could try exploring.

  • Analyze market prices at different timeframes. Low-level data like one-minute bars are noisy (as they include lots of small price movements caused by individual trades), and it is like looking at the market using a microscope. At larger scales, such as one-hour or one-day bars...

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