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drl-zh discussion
drl-zh reviews and mentions
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Deep Reinforcement Learning: Zero to Hero
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 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
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alessiodm/drl-zh is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of drl-zh is Jupyter Notebook.
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