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
Contributors
Preface
Other Books You May Enjoy
Index

Trading


There are lots of financial instruments traded on markets every day: goods, stocks, and currencies. Even weather forecasts can be bought or sold using so-called “weather derivatives," which is just a consequence of the complexity of the modern world and financial markets. If your income depends on future weather conditions, like a business growing crops, then you might want to hedge the risks by buying weather derivatives. All these different items have a price which is changed over time. Trading is an activity of buying and selling financial instruments with different goals, like making profit (investment), gaining protection from future price movement (hedging) or just getting what you need (like buying steel for your manufacture or exchanging USD to JPY to pay a contract).

Since the first financial market was established, people have been trying to predict future price movements, as this promises lots of benefits, like “profit from nowhere” or protecting capital from sudden market...