alessiodm

5 ms·

1. 1.▲
   Zero-to-Hero Deep Reinforcement Learning Course: Update with Advanced Topics(drlzh.ai)

   3 points by alessiodm 1y ago|1 comments
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   by alessiodm 1y ago

   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
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   by alessiodm 2y ago

   Really happy to hear you enjoyed the notebooks! And thank you very much for the patch in the simulate_mdp for the cliff world!
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   by alessiodm 2y ago

   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!
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   by alessiodm 2y ago

   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 (
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   by alessiodm 2y ago

   Thank you! I'll be be curious if / how these notebooks help and how your experience is! Any feedback welcome!
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   by alessiodm 2y ago

   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
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   by alessiodm 2y ago

   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
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   by alessiodm 2y ago

   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" al
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    by alessiodm 2y ago

    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
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    by alessiodm 2y ago

    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 the
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    by alessiodm 2y ago

    Didn't know that, but now I have an excuse to go watch a movie :D
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    by alessiodm 2y ago

    Thank you, I appreciate it.
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    by alessiodm 2y ago

    Thank you!
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    by alessiodm 2y ago

    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.
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    by alessiodm 2y ago

    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. Sor
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    by alessiodm 2y ago

    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
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    by alessiodm 2y ago

    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
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    by alessiodm 2y ago

    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 "
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    by alessiodm 2y ago

    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 advanc
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    Deep Reinforcement Learning: Zero to Hero(github.com)

    535 points by alessiodm 2y ago|47 comments
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    by alessiodm 2y ago

    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 per