Reinforcement Learning is AI for playing games, planning actions, and doing things in the real world. It's extremely powerful, but also a fun and interesting topic to learn. I'm teaching myself now.
I was always intimidated by all the esoteric jargon and math in RL. The classic Sutton and Barto text clears that up nicely, though. It's one of the best text books I have ever studied, and it reveals RL is much less complex than it sounds.
I think the excessive mathiness comes from the question "is this policy optimal?" which is a natural place to start, and has been fruitful for the field.
Except, in practice it's rare we can find optimal policies for practical problems. Also, our mathematical formalisms often don't fit, which is why there are so many variations based on what information is available, and what you want to focus on. This produces the tangled mess of names and notations, but really it's just the same idea from subtly different angles.