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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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Advantages of NAS


The biggest advantage of NAS is that one does not need to spend copious amounts of time designing a neural network for a particular problem. This also means that those who are not data scientists can also create machine learning agents as long as they can prepare data. In fact, Google has already productized this framework as Cloud AutoML, which allows anyone to train customized machine learning models with minimum effort. According to Google, Cloud AutoML provides the following benefits:

  • Users only need to interact with a simple GUI to create machine learning models.
  • Users can have Cloud AutoML annotate their own datasets if they are not labeled already. This is similar to Amazon's Mechanical Turk service.
  • Models generated by Cloud AutoML are guaranteed to have high accuracy and fast performance.
  • Easy end-to-end pipeline for uploading data, training and validating the model, deploying the model, and creating a REST endpoint for fetching predictions.

Currently, Cloud AutoML...

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