Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
26
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27
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 others. Incorporating them into the observation could be a challenging problem.
  • Market prices are usually analyzed 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, you can see large, long trends in data movement, which could be extremely important for price...