Deep Reinforcement Learning Hands-On

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. ...