The Deep Reinforcement Learning algorithm Advantage Weighted Regression (AWR) has been around since October 2019.
The original implementation of the novel algorithm in software was done in Python, using the library TensorFlow.

My own implementation of AWR is written with PyTorch, making it very useful for anyone using this library in their DRL project.
My implementation is the first of its kind on GitHub. It also entails a comprehensive training framework which allows for customisation on multiple levels (hyper-parameters, problems, network architectures) and spares the user the usual overhead of setting all that up.

Below you can see the result of my AWR implementation as applied to the Pendulum problem:

I participated in a research project at the TUM Computer Vision Group for one year during my Bachelor's studies — half a year in the form of a practical course and half a year for my Bachelor's thesis.
Both of these projects were under the supervision of Mr. Vladimir Golkov.

The project concerned itself with the optimisation and creation of Evolutionary Algorithms through the use of Deep Reinforcement Learning.
We used modern DRL algorithms such as AWR and SAC to tackle this set of problems.

The project is soon to culminate in its first publication, which will be posted here once available.

I have written and published a small number of blog posts on Medium, about a range of topics.

One of those posts was called "Getting Started with Deep Reinforcement Learning".
It describes the process one might follow when starting to engage with and tackle projects in DRL. It was marked by the Medium content moderators as a Top Story in Machine Learning and published in Data Driven Investor.

Neuroevolution of Augmenting Topologies (NEAT) is a genetic algorithm that generates artificial neural networks from scratch using principles of evolution.
Some academic implementations have been published, however I wrote this implementation myself, using Python to execute the algorithm and Pyglet for rendering.

This was my first AI project, which is why it's listed here.
Feel free to check it out on GitHub or watch a demo here:

Together with four student colleagues of mine I founded the start-up company "ValveShelf" at the end of 2017 in Passau.

We produced a fully automatic cocktail machine, which could be controlled by a smartphone app which was available via AppStore and PlayStore.
Our business model was renting the machine to private customers and companies hosting small parties and celebrations. People could create and order their own cocktails and have the machine mix it for them.

We also completed the Honours Degree in Digital Technology and Entrepreneurship at the University of Passau with this project.
We eventually terminated our pursuit of the project, favoring our academic careers.