One thing I found about Julia which turned me off was the library used for distributions used 64bit floats while that used for neural Nets used less precision, so it's hard to connect the dots there. Also there wasn't a lot of great tooling for RL
If you specify a distribution using, say, Float32s, you'll get a distribution in Float32s. I'm guessing the machine learning example used Float32s so they would better take advantage of a GPU. It's almost always true in Julia that you can combine your choice of numeric type with other neat packages without the author of either one being aware of the other. Want to simulate ODEs in Float32 for speed on GPU, in Float64 for GPU, or if you have an exotic need for high accuracy use something bigger but slower, you can do it.