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

Summary

RL is one of the fundamental paradigms under the umbrella of machine learning. The principles of RL are very general and interdisciplinary, and they are not bound to a specific application.

RL considers the interaction of an agent with an external environment, taking inspiration from the human learning process. RL explicitly targets the need to explore efficiently and the exploration-exploitation trade-off appearing in almost all human problems; this is a peculiarity that distinguishes this discipline from others.

We started this chapter with a high-level description of RL, showing some interesting applications. We then introduced the main concepts of RL, describing what an agent is, what an environment is, and how an agent interacts with its environment. Finally, we implemented Gym and Baselines by showing how these libraries make RL extremely simple.

In the next chapter, we will learn more about the theory behind RL, starting with Markov chains and arriving at MDPs...