Want to learn Reinforcement Learning from scratch for mini-project

Hello everyone,

I’m quite new to Julia but have some basic knowledge of other programming languages.

As a project for myself I want to train an RL algorithm for a boardgame. It’s a push-your-luck type of game, where you need to asses risk/rewards and therefore I think there are 100% dominant winning strategies which can be learned.

On the same time this seems like a nice opportunity to get more familiar with Julia.

I have theoretical background knowledge on machine learning but not RL specific. So I basically want to teach myself Reinforcement Learning from scratch as well as the Julia specifics.
Anybody has advice on how to approach this? I got quite some time on my hand during semester break. I just started reading “Reinforcement Learning: An Introduction” from Sutton & Barto, but I wonder how other people would approach this.

Thanks for any input/experiences :slight_smile:


I don’t have experience implementing RL in Julia, but I’ll just share a few links I’ve come across:


There is a repo implementing solutions for all problems in “Reinforcement Learning: An Introduction” using the library ReinforcementLearning.jl, so that could maybe be a good start.

There is also the library POMDPs.jl which also seem to cover many similar topics.


Hi Lighfe,

As for books there are:

There are interesting videos:


Thank you for the many resources. Would you recommend the books above “Reinforcement Learning - An Introduction”? Or rather as a go to for certain questions/problems that might occur?

I guess that you are also addressing me in this question, however, I am very far from being an expert on those topics. I like all those books. I started with Sutton and Barto and I decided to send an email to @jonathan-laurent, however, it happened that he was busy. :- ) So I started digging deeper and I read this piece (not every line of course) by Kochenderfer, Wheeler and Wray and I decided to send an email to @findmyway. Slightly less, however, the same outcome, busy. :- ) So I came by this book by Bellemare, Dabney and Rowland. IMO, it outlines traditional and new reinforcement learning ideas really well, thus I think it might be a great choice as the first book. I’m also interested in quantum stuff a little bit and somehow I spotted some parallelism. This I think makes this book even more interesting, especially that the research on quantum reinforcement learning is so far rather limited. :- )

I just recalled one more book that I liked (there are of course many others). Its not quite using Julia, however, the book is really written from a practical point of view. Hope you find the info that I tried to provide useful.