I agree to an extend. The current state of RL algorithms are more fragmented and have limited application comparing to something like a CNN.
So unless one only wants to learn how to play Atari, they won't be able to "fix" an algorithm if it breaks down in an untested environment. E.g. a non deterministic sparse reward game.
If one day there is a generalized algorithm that can solve large set of RL problems, then I think a high level intuition is probably enough to use RL, but for now, I'd say RL is definitely not for the faint of heart.
True - I often make the heretical argument that SL vs RL is just a question of where the labeling comes from :-) You are correct that the tooling is weaker in this space, but it is growing - my point is only that there's a difference between knowing how to use the tool and knowing how to make the tool - making the tool can liquify your brain -- using someone else's tool (assuming its a good tool) will simply give you headaches from time to time :-D
So unless one only wants to learn how to play Atari, they won't be able to "fix" an algorithm if it breaks down in an untested environment. E.g. a non deterministic sparse reward game.
If one day there is a generalized algorithm that can solve large set of RL problems, then I think a high level intuition is probably enough to use RL, but for now, I'd say RL is definitely not for the faint of heart.