I've spent the last six months replicating the paper "Champion-level drone racing using deep reinforcement learning" and now I'm writing down the blog posts I wish I had along the way.
Any feedback is welcome, especially as I'm a bit unsure if I struck the right balance between being concise and not requiring too many prerequisites.
Also if you're working on RL and robotics (especially aerial), let's connect!
In general, I think I'd try to go for a black-box/grey-box model based on real data rather than e.g. CFD-based, as I don't think you can run CFD at sufficient accuracy for real-time control anyway. For that, I would look at https://rpg.ifi.uzh.ch/docs/TRO26_Bauersfeld.pdf or https://rpg.ifi.uzh.ch/docs/RSS21_Bauersfeld.pdf
The other effects are more apparent, especially settling with power, the tendency to pitch back as you go faster, and translational lift. Modeling all of these accurately turns into a fluid simulation, so probably you'd just want a heuristic.