Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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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:

  1. 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.

  2. 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 prediction.

  3. More training data is needed. One year of data for one stock...