I’ve released a major expansion of my open-source deep reinforcement learning course. Last year's initial release got positive feedback, so I've added a new module with advanced topics and practical productionization techniques by curating and refining materials I collected over the years. This final update includes hands-on implementations of RND, AlphaZero, RLHF, MBPO, and more. I hope it's a valuable resource for the community.
Great feedback, I didn't even think about that the TODOs could be indeed confusing! I updated the instructions in the README.md calling them out explicitly as the coding sections to be completed. Thanks again!
Thank you so much for this feedback! Indeed, this is definitely confusing in the notebook. I pushed a small commit to make it a little bit more clear that the non-determinism comes from the probabilistic nature of the environment dynamics (and not b/c the agent chooses a different action by mistake).
As a side note, initially I meant to go through it in a video to fill the gaps in the text with my voice. But given that I didn't have time for those, I am fixing those gaps first :) Thanks again!
Thank you. It is true, indeed the material does assume some prior knowledge (which I mention in the introduction). In particular: being proficient in Python, or at least in one high-level programming language, be familiar with deep learning and neural networks, and - to get into the theory and mathematics (optional) - basic calculus, algebra, statistics, and probability theory.
Nonetheless, especially for RL foundations, I found that a practical understanding of the algorithms at a basic level, writing them yourself, and "playing" with them and their results (especially in small toy settings like the grid world) provided the best way to start getting a basic intuition in the field. Hence, this resource :)
Yes, the material relies heavily on Python. I intentionally used popular open-source libraries (such as Gymnasium for RL environments, and PyTorch for deep learning) and Python itself given their popularity in the field, so that the content and learnings could be readily applicable to real-world projects.
The theory and algorithms per-se are general: they can be re-implemented in any language, as long as there are comparable libraries to use. But the notebooks are primarily in Python, and the (attempted) "frictionless" learning experience would lose a bit if the setup is in a different language, and it'll likely take a little bit more effort to follow along.
I took the Deep Learning course [1] by deeplearning.ai in the past, and their resources where incredibly good IMHO. Hence, I would suggest to take a look at their NLP specialization [2].
+1000 to "Neural networks: zero to hero" already mentioned as well.
Thank you so much! Unfortunately, that is a mistake in the README that I just noticed (thank you for pointing it out!) :( As I mentioned in the first post, I didn't get to make the YouTube videos yet. But it seems the community would be indeed interested.
I will try to get to them (and in the meantime fix the README, sorry about that!)
Thank you for the amazing links as well! You are right that the article [1] is 6 years old now, and indeed the field has evolved. But the algorithms and techniques I share in the GitHub repo are the "classic" ones (dating back then too), for which that post is still relevant - at least from an historical perspective.
You bring up a very good point though: more recent advancements and assessments should be linked and/or mentioned in the repo (e.g., in the resources and/or an appendix). I will try to do that sometime.
Thanks a lot. It makes me feel better to hear that the post is not completely confusing and appropriating - I really didn't mean that, or to use it as a trick for attention.
TL;DR: If more folks feel this way, please upvote this comment: I'll be happy to take down this post, change the title, and either re-post it or just don't - the GitHub repo is out there - that that should be more than enough. Sorry again for the confusion (I just upvoted it).
I am deeply sorry about the confusion. And the last thing I intended was to grab any attention away from Andrej, and / or being confused with him.
I tried to find a way to edit the post title, but I couldn't find one. Is there just a limited time window to do that? If you know how to do it, I'd be happy to edit it right away in case.
I didn't even think this post would get any attention at all - it is my first post indeed here, and I really did it just b/c if anybody could use this project to learn RL I was happy to share.
RL can be massively disappointing, indeed. And I agree with you (and with the amazing post I already referenced [1]) that it is hard to get it to work at all. Sorry to hear you have been disappointed so much!
Nonetheless, I would personally recommend even just learning the basics and fundamentals of RL. Beyond supervised, unsupervised, and the most-recent and well-deservedly hyped semi-supervised learning (generative AI, LLMs, and so on), reinforcement learning indeed models the learning problem in a very elegant way: an agent interacting with an environment and getting feedback. Which is, arguably, a very intuitive and natural way of modeling it. You could consider backward error correction / propagation as an implicit reward signal, but that would be a very limited view.
On a positive note, RL has very practical sucessful applications today - even if in niche fields. For example, LLM fine-tuning techniques like RLHF successfully apply RL to modern AI systems, companies like Covariant are working on large robotics models which definitely use RL, and generally as a research field I believe (but I may be proven wrong!) there is so much more to explore. For example, check Nvidia Eureka that combines LLM to RL [2]: pretty cool stuff IMHO!
Far from attempting to convince you on the strength and capabilities of DRL, just recommending folks to not discard it right away and at least give it a chance to learn the basics, even just for an intellectual exercise :) Thanks again!
Thank you very much! I'd be really interested to know if your agents will eventually make progress, and if these notebooks help - even if a tiny bit!
If you just want to see if these algorithm can even work at all, feel free to jump on the `solution` folder and pick any algorithm you think could work and just try it out there. If it does, then you can have all the fun rewriting it from scratch :) Thanks again!
Thanks a lot, and another great suggestion for improvement. I also found that the common advice is "tweak hyperparameters until you find the right combination". That can definitely help. But usually issues hide in different "corners", both of the problem space and its formulation, the algorithm itself (e.g., just different random seeds have big variance in performance), and more.
As you mentioned, in real applications of DRL things tend to go wrong more often than right: "it doesn't work just yet" [1]. And my short tutorial definitely lacks in the area of troubleshooting, tuning, and "productionisation". If I carve time for expansion, this will likely make top of list. Thanks again.
Thank you so much! And very good advice: I have an extremely brief and not-descriptive list in the "Next" notebook, initially intended for that. But it definitely falls short.
I may actually expand it in a second "more advanced" series of notebooks, to explore model-based RL, curiosity, and other recent topics: even if not comprehensive, some hands on basic coding exercise on those topics might be of interest nonetheless.
While trying to learn the latest in Deep Reinforcement Learning, I was able to take advantage of many excellent resources (see credits [1]), but I couldn't find one that provided the right balance between theory and practice for my personal experience. So I decided to create something myself, and open-source it for the community, in case it might be useful to someone else.
None of that would have been possible without all the resources listed in [1], but I rewrote all algorithms in this series of Python notebooks from scratch, with a "pedagogical approach" in mind. It is a hands-on step-by-step tutorial about Deep Reinforcement Learning techniques (up ~2018/2019 SoTA) guiding through theory and coding exercises on the most utilized algorithms (QLearning, DQN, SAC, PPO, etc.)
I shamelessly stole the title from a hero of mine, Andrej Karpathy, and his "Neural Network: Zero To Hero" [2] work. I also meant to work on a series of YouTube videos, but didn't have the time yet. If this posts gets any type of interest, I might go back to it. Thank you.
P.S.: A friend of mine suggested me to post here, so I followed their advice: this is my first post, I hope it properly abides with the rules of the community.