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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Chapter 8. Stocks Trading Using RL

Rather than learning new methods to solve toy reinforcement learning (RL) problems in this chapter, we’ll try to utilize our deep Q-network (DQN) knowledge to deal with the much more practical problem of financial trading. I can’t promise that the code will make you super rich on the stock market or Forex, because the goal is much less ambitious: to demonstrate how to go beyond the Atari games and apply RL to a different practical domain.

In this chapter, we’ll implement our own OpenAI Gym environment, which simulates the stock market, and apply the DQN method that we’ve just learned in Chapters 6, Deep Q-Networks, and Chapter 7, DQN Extensions, to train the agent that will trade stocks to maximize the profit.