Training the Continuous Lunar Lander with Reinforcement Learning, RLLib and PPO
For an upcoming blog post, I would like to have a robotic arm to land a Lunar Lander autonomously. In Part 1 I explained how we can build such a robotic arm already, but now we need to be able to go deeper into how we are able to train an environment in a simulation environment (before deploying it on a physical device).
Autonomously Landing a Lunar Lander with an Xbox Controller Robotic Arm - Part 1
Creating an Xbox Robot Arm is something that I've been wanting to do ever since I saw the post by Kevin Drouglazet who was able to utilize an Xbox Controller with an arm he created and was so friendly to publish the design files for (I am definitely not a hardware designer 😅) - thanks for that Kevin!
Getting Xbox Controller inputs through Node.js
For an upcoming Blog Post I wanted to connect my Xbox controller and start reading the input values through a simple node.js script that I could send back to an Arduino Nano. This entire process however seemed to be quite "interesting" to find suitable libraries and long-term supported solutions.
Digital Twins - Creating an Open-Source Platform
In a previous blog post I introduced the concept of creating a Digital Twin representation utilizing Dapr's Virtual Actors. Now, this was a theoretical post on how feasible this could be. Of course anything theoretical should also be put in practice, which is what I have been doing this weekend 😉.
Digital Twins - Utilizing Virtual Actors through Dapr
As stated before, "A Digital Twin is a virtual representation of a physical object". This virtual representation should strive towards being an exact copy of the physical object in both state and logic associated with it.
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
After explaining the end-to-end concept of creating a Digital Twin Reinforcement Learning environment I wanted to go into a deeper explanation of how the first part of this can be done.
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.
Internet Of Things - Digital Twins Explained
Looking at the history in manufacturing, we saw that many manufacturing companies were deploying statistical models on-edge (e.g. based on parameters such as temperature, pressure and speed, do we send an alert or not), having the main impact on their production volume. If they can then squeeze out this extra bit of improvement, they would thus be able to optimize this process and have a growth in production volume (and net earnings accompanied with it).