Xavier Geerinck

Machine Teaching through Azure Project Bonsai - Series Overview

Autonomous Platforms are being deployed all around us, optimizing our daily processes and improving their efficiency and stability without the need of human intelligence. They learn by themself, iteratively improving themselves and staying ahead of the competition.

Azure Project Bonsai - Physical CartPole Project

In my previous post I revisited the Bonsai platform. Now it's time to actually start preparing to put it into production! (this will become more clearer over time 😉) To start off with a spoiler: Project Bonsai - It's impressive! 😎

The Azure Project Bonsai Platform Revisited

Implementing Deepmind's MuZero Algorithm with Python

Setting up Stable Baselines with Python 3.7 and WSL 2

I've been playing around a bit with Stable Baselines lately for an upcoming project. However while playing, I encountered some issues seeing that Stable Baselines currently requires Tensorflow <= 1.14 which is only supported through Python < 3.8. Due those difficulties, I thought it would be interesting to share how I set up my personal development environment from scratch to play around with Stable Baselines.

Autonomously Landing a Lunar Lander with an Xbox Controller Robotic Arm - Part 2

Training the Continuous Lunar Lander with Reinforcement Learning, RLLib and PPO

Autonomously Landing a Lunar Lander with an Xbox Controller Robotic Arm - Part 1

Unity ML-Agents - An Introduction

Reinforcement Learning with the Bonsai Platform

The Bonsai Machine Teaching platform has been released! Promising an easy to use environment for end-to-end Reinforcement Learning projects, starting with simulator selection / integration to algorithm configuration and training.

Roadwork RL - A Multi-Language Reinforcement Learning Environment

A Multi-Language Reinforcement Learning Digital Twin Environment

One of the ideas I have been playing around with the last couple of months is the combination of Digital Twins and Reinforcement Learning. This is an experimental idea where I would love to hear your opinions about it (feel free to comment below, send me an email or reach out to me on Social Media such as Twitter / LinkedIn), and that will be refined over the coming months.

Facebook ReAgent - An End-to-End Use Case

Facebook's Open-Source Reinforcement Learning Platform - A Deep Dive

Facebook decided to open-source the platform that they created to solve end-to-end Reinforcement Learning problems at the scale they are working on. So of course I just had to try this ;) Let's go through this together on how they installed it and what you should do to get this working yourself.

Reinforcement Learning - Terminology

Writing a C# SDK for the OpenAI Gym using .NET Core

OpenAI Gym Problems - Solving the CartPole Gym

In a previous post we set-up the OpenAI Gym to interface with our Javascript environment. Let's now look at how we can use this interface to run the CartPole example and solve it with the theory that we learned in previous blog posts.

Dividing numbers into equal buckets or bins through Bucketization

A common practice in Reinforcement Learning is to go from a continuous space towards a discrete space. What does this mean? Take for example a range of numbers between $]-3, 3[$, if we want to represent all the numbers that fit in this range as a state then we would have $\infty$ numbers being represented or $\infty$ states.

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"?

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)