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

OpenAI Baselines

So far, we have studied the two different frameworks that allow us to solve reinforcement learning problems (OpenAI Gym and OpenAI Universe). We also studied how to create the "brain" of the agent, known as the policy network, with TensorFlow.

The next step is to train the agent and make it learn how to act optimally, only through experience. Learning how to train an RL agent is the ultimate goal of this book. We will see how most advanced methods work and find out about all their internal elements and algorithms. But even before we find out all the details of how these approaches are implemented, it is possible to rely on some tools that make the task more straightforward.

OpenAI Baselines is a Python-based tool, built on TensorFlow, that provides a library of high-quality, state-of-the-art implementations of reinforcement learning algorithms. It can be used as an out-of-the-box module, but it can also be customized and expanded. We will be using it...