Fast Deep Reinforcement Learning Course
I worked on this applied Deep Reinforcement Learning course for the better part of 2021. I made a Datacamp course [0] before, and this served as my inspiration to make an applied Deep RL series. Normally, Deep RL courses teach a lot of mathematically involved theory. You get the practical applications near the end (if at all). I have tried to turn that on its head. In the top-down approach, you learn practical skills first, then go deeper later. This is much more fun. This course (the first in a planned multi-part series) shows how to use the Deep Reinforcement Learning framework RLlib to…
In plain words
Fast Deep Reinforcement Learning Course is an educational series that teaches applied deep reinforcement learning through a practical, top-down approach. Students learn to use the RLlib framework to solve OpenAI Gym environments, building hands-on skills before diving into mathematical theory. Designed for developers interested in reinforcement learning, this course prioritizes working implementations over theoretical foundations, similar to learning deep learning through frameworks like Keras. It forms the first part of a planned multi-part series.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I worked on this applied Deep Reinforcement Learning course for the better part of 2021. I made a Datacamp course [0] before, and this served as my inspiration to make an applied Deep RL series. Normally, Deep RL courses teach a lot of mathematically involved theory. You get the practical applications near the end (if at all). I have tried to turn that on its head. In the top-down approach, you learn practical skills first, then go deeper later. This is much more fun. This course (the first in a planned multi-part series) shows how to use the Deep Reinforcement Learning framework RLlib to solve OpenAI Gym environments. I provide a big-picture overview of RL and show how to use the tools to get the job done. This approach is similar to learning Deep Learning by building and training various deep networks using a high-level framework e.g. Keras. In the next course in the series (open for pre-enrollment), we move on to solving real-world Deep RL problems using custom environments and various tricks that make the algorithms work better [1]. The main advantage of this sequence is that these practical skills can be picked up fast and used in real life immediately. The involved mathematical bits can be picked up later. RLlib is the industry standard, so you won't need to change tools as you progress. This is the first time that I made a course on my own. I learned flip-chart drawing to illustrate the slides and notebooks. That was fun, considering how much I suck at drawing. I am using Teachable as the LMS, Latex (Beamer) for the slides, Sketchbook for illustrations, Blue Yeti for audio recording, OBS Studio for screencasting, and Filmora for video editing. The captions are first auto-generated on YouTube and then hand edited to fix errors and improve formatting. I do the majority of the production on Linux and then switch to Windows for video editing. I released the course last month and the makers of RLlib got in touch to show their approval. That's the best thing to happen so far. Please feel free to try it and ask any questions. I am around and will do my best to answer them. [0] https://www.datacamp.com/courses/unit-testing-for-data-scien... [1] https://courses.dibya.online/p/realdeeprl
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Dev tools · 1d ago · opentrailpaper.com
Launched alongside, June 2022
the whole month →- AF
I've seen some interests in (simulation) video games here on HN so I thought I'd share a short version of our story. More than 6 years ago, me and my friend from university were playing around with an idea of making a game we always wanted to play. We worked on it on weekends but the progress was quite slow, especially due to so many dead ends and wasted effort. Eventually however, we solidified our direction and decided to take the risk to resign from our well paid SWE jobs and work on it full time. It took more than a year but yesterday we have finally released it on Steam:…
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