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.
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've gone through the first three notebooks today and enjoyed them a lot. First time I've tried the Atari gymnasium and that was really satisfying and fun. Thank you.
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.
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.
[1] https://github.com/alessiodm/drl-zh/blob/main/00_Intro.ipynb [2] https://karpathy.ai/zero-to-hero.html