Book Image

The Reinforcement Learning Workshop

By : Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, Saikat Basak
Book Image

The Reinforcement Learning Workshop

By: Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, Saikat Basak

Overview of this book

Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, youÔÇÖll be guided through different RL environments and frameworks. YouÔÇÖll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youÔÇÖve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youÔÇÖll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, youÔÇÖll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, youÔÇÖll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.
Table of Contents (14 chapters)
Preface
Free Chapter
2
2. Markov Decision Processes and Bellman Equations

Simple Regression Using TensorFlow

This section will explain, step by step, how to successfully tackle a regression problem. You will learn how to take a preliminary look at the dataset to understand its most important properties, as well as how to prepare it to be used during training, validation, and inference. Then, a deep neural network will be built from a clean sheet using TensorFlow via the Keras API. This model will then be trained and its performance will be evaluated.

In a regression problem, the aim is to predict the output of a continuous value, such as a price or a probability. In this exercise, the classic Auto MPG dataset will be used and a deep neural network will be trained on it to accurately predict car fuel efficiency, using no more than the following seven features: Cylinders, Displacement, Horsepower, Weight, Acceleration, Model Year, and Origin.

The dataset can be thought of as a table with eight columns (seven features, plus one target value) and as...