Xavier Geerinck

Getting started with DotNet Core

Bellman Equations

Summary

The Markov Property, Chain, Reward Process and Decision Process

As seen in the previous article, we now know the general concept of Reinforcement Learning. But how do we actually get towards solving our third challenge: "Temporal Credit Assignment"?

Ordinary Least Squares (OLS)

Let's start by defining the goal of our algorithm, what do we want to achieve with our OLS algorithm? Well if we have data points in a region (or XY-axis), then we want to be able to find an equation that fits as closely to these points as possible. We thus want to minimize the sum of the squared residuals. Just take a look at the picture below to see an illustration of this.

Getting AMQP to work in your browser

For one of my customers, I had to be able to connect to an EventHub through the browser, so how did I do this? So we know that EventHub works with the AMQP protocol, so what if we could get this working in the frontend?

Installing OpenAI Gym in a Windows Environment

Reinforcement learning does not only requires a lot of knowledge about the subject to get started, it also requires a lot of tools to help you test your ideas. Since this process is quite lengthy and hard, OpenAI helped us with this. By creating something called the OpenAI Gym, they allow you to get started developing and comparing reinforcement learning algorithms in an easy to use way.

Multi-armed bandit framework

To start solving the problem of exploration, we are going to introduce the Multi-armed bandits framework. But what exactly does this solve? Just think that you are executing a clinical trial with 4 pills. You know that the pills have a survival rate but you don't know what that survival rate is. Your goal: find the pill with the highest chance of survival in X trials.

An introduction to Reinforcement Learning (RL)

Connect to your Docker Daemon through the Windows Subsystem for Linux (WSL)

Do you have Windows installed and you love using the Linux Subsystem on it? (commonly known as WSL) Well let me tell you the steps to achieve this.

An introduction to Machine Learning (ML)

Every few decennia there is a new cool kid around the block that makes an impact on the current world, an impact that is so big that we will have to adapt. Starting with the introduction of mainframe computing in 1950, the pc in 1975, the internet in 1980 and the introduction of mobile phones in 2007, towards the current age of Big Data which started in 2012.