Show HN: 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 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

Show HN: Fast Deep Reinforcement Learning Course
courses.dibya.online

This looks great! Thank you for all the thought and effort you have put into it.

I am currently working on a project where I need to use RLlib for a capacity planning problem. Looks like I will learn a thing or two over the weekend.

I will eventually need to use a custom environment, so it's great to see it's included in your roadmap. Most courses I have seen totally ignored that. Fancy Atari envs are great for practice and have wow factor, but you need a custom environment to do anything resembling real work.

Would I need a beefy GPU for the coding challenges?

Congrats on the launch. I have seen your Deep RL tutorials circulating on YouTube. I like your presentation style: crisp and precise.

I am sorry about that. Unfortunately, the same thing happens in Firefox when the tracking protection is set to "strict".

This is apparently happening after Teachable updated their video player. Earlier, they used Wistia. Now they use Hotmart.

I have informed Teachable about this issue. They said they will look into it.

The current workaround would be to use Chrome or Firefox (with tracking protection set to a level below "strict").

To be honest though, the practical side of things of RL can be a hit-and-miss in terms of "fun" depending on the person. It requires a lot of manual hand tuning, reward shaping, hyperparameter tuning, and general trial-and-error to make an agent do a seemingly simple-enough task, and these tricks are more heuristically and haphazardly done than what you would expect from more "conventional" programming. It is fun for the right people (who loves tinkering with stuff and also have the perseverance to continually run RL experiments that can last hours or even days). But I would imagine many getting bored by the whole experience. (Pssst.... I was one of them, switched to doing something else in the middle of grad school)

By the way, RLlib is good if you want to try out simple experiments with well-established RL algorithms, but it's really awful to use when you want to modify the algorithm even just a little bit. So it's not bad for beginner-level tutorials, but once you get the basics it might be very frustrating later on. I would recommend simpler frameworks like Stable Baselines 3 (https://stable-baselines3.readthedocs.io/en/master/ ) for a much more stable experience, if you have gained a fair bit of Python/ML programming skills at hand and don't have trouble reading well-maintained library code.