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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In our example, we’ll use the Russian stock market prices for the period of 2015-2016, which is placed in Chapter08/data/ch08-small-quotes.tgz and has to be unpacked before model training.

Inside the archive, we have CSV files with M1 bars, which means that every row in the CSV corresponds to a single minute in time and price movement during this minute is captured with four prices: open, high, low, and close. Here, an open price is the price at the beginning of the minute, high is the maximum price during the interval, low is the minimum price, and the close price is the last price of the minute time interval. Every minute interval is called bar and allows us to have an idea of price movement within the interval. For example, in the YNDX_160101_161231.csv file (which is Yandex company stocks for 2016), we have 130k lines of this form: