January 5, 2021 | ai ai-ml ai-rl

Implementing Deepmind's MuZero Algorithm with Python

Deepmind has achieved a huge milestone by publishing its latest paper around Reinforcement Learning in Nature - 23/DEC/2020. How they were able to train a Reinforcement Learning algorithm that masters Go, Chess, Shogi and Atari without needing to be told the rules.

September 20, 2020 | ai ai-ml

Monitoring the Kubernetes Nginx Ingress Controller with Prometheus and Grafana

In a previous article I explained how we can set-up an Nginx Kubernetes Ingress Controller, but how can we now monitor this? This is what I would like to tackle in this article, on how we are able to utilize Prometheus and Grafana to start visualizing what is happening on our Ingress Controller.

May 27, 2020 | ai ai-ml ai-rl

Unity ML-Agents - An Introduction

Seeing the rising popularity of the ML-Agents framework and them having achieved Release 2 - congratulations! - I decided to start by running one of the examples and documenting it.

October 30, 2019 | azure ai ai-rl ai-ml

Facebook ReAgent - An End-to-End Use Case

Facebook decided to release their end-to-end applied reinforcement learning platform called ReAgent, after reading their vision on this, I have to say that I am completely hooked! They are providing an excellent view of Reinforcement Learning and the future adoption of it. But why is this and how can we get started with it?

August 10, 2018 | ai ai-ml ai-rl

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

When we take a look at the OpenAI Gym on Github (https://github.com/openai/gym-http-api), we see that it does not have bindings available for C#. Now since I am a firm believer of .NET Core and what it brings to developer ecosystem, I decided to write one myself (https://github.com/Xaviergeerinck/dotnetcore-sdk-openai). Using what I learned in my previous blog post How to write a SDK in dotnet Core I created one that looks like this for the main method:

May 1, 2018 | ai ai-ml ai-rl

An introduction to Reinforcement Learning (RL)

So as we learned in the intro to Machine Learning, Reinforcement Learning is this technique where we have an agent who will take specific actions on an environment to try to reach an optimal state. But how can we illustrate this? Take a look at the following picture.

Xavier Geerinck © 2020

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