Reinforcement Learning Digest Part 1: Introduction & Finite Markov Decision Process framework

Introduction

Reinforcement learning is an important type of machine learning used in vast range of applications and fields including robotics, genetics, financial applications and recommendation systems to mention a few. In this series of articles, I aim at taking the reader into a journey to learn enough about this topic. The goal is to build knowledge in reinforcement learning starting from basic principles and gradually get to more advanced aspects of reinforcement learning. The articles will have a balance theory and practical demos which can help to practice theory learnt and cement understanding. So let us start the journey…

Definition

Reinforcement learning can be defined as follows:

”Reinforcement learning is an area of machine learning concerned with how software agents ought to take actionsin an environment in order to maximize some notion of cumulative reward.”

- Wikipedia

From this definition, we see that we have a software agent that interacts with an environment by taking actions which results in an immediate reward. it is the goal of the reinforcement learning is for the agent to learn how to maximize cumulative rewards obtained from taking sequence of such actions. One should note that actions with highest immediate rewards will result in optimal overall rewards. Therefore, the goal of…