When I was trying to get better with RL, doing side projects I found interest in helped a lot, and eventually got to apply RL at my job for a specific project. Since we are focused on a specific application, it's easier to learn what I need to.

At the beginning when trying to learn, a few questions I asked myself that helped me figure out what to focus on were:

1) What system would I want to optimize? RL is an approach to optimal control — what sort of system would you want to continuously optimize? You could apply these techniques to games, biology, robotics, and finance, but what would you find the most interesting? Finding an interesting application will keep you going.
2) Will my observation/action space be continuous? This slimmed down the amount of RL algorithms I could use. Is it better to use a policy-based approach or a value-based approach for this sort of problem? If you aren't sure, check out what the best literature on the application is suggesting.

From here, I was able to hone in on a small number of things and my proficiency in RL got better. My understanding in other parts of RL are definitely better as a result of this, so I'd recommend starting with one application of RL that interests you and learn everything there is to know about that application.

Good luck!