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Python Reinforcement Learning Projects

Python Reinforcement Learning Projects

By : Sean Saito, Yang Wenzhuo , Shanmugamani
5 (1)
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Python Reinforcement Learning Projects

Python Reinforcement Learning Projects

5 (1)
By: Sean Saito, Yang Wenzhuo , Shanmugamani

Overview of this book

Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.
Table of Contents (12 chapters)
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Summary


In conclusion, machine learning can be applied to several industries and can be applied very efficiently in financial markets, as you saw in this chapter. We can combine different models, as we did with reinforcement learning and time series, to produce stronger models that suit our use cases. We discussed the use of reinforcement learning and time series to predict the stock market. We worked with an actor-critic model that determined the best action, based on the state of the stock prices, with the aim of maximizing profits. In the end, we obtained a result that boasted an overall profit and included increasing profits over time, indicating that the agent learned more with each state.

In the next chapter, you will learn about the future areas of work. 

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