[achandra03]

This looks really interesting! I tried exploring deep RL myself some time ago but could never get my agents to make any meaningful progress, and as someone with very little stats/ML background it was difficult to debug what was going wrong. Will try following this and seeing what happens!

[alessiodm]

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!

[3abiton]

Thanks for sharing your work!

[barrenko]

I mean, resources like these are great, but RL in itself is quite dense and topic heavy, so not sure there is any way to reduce the inherent difficulty level, any beginner should be made clear to that. That's my primary gripe with ML topics (especially RL related).

[alessiodm]

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 :)