Honestly the best way is starting with implementing a Q table for some small grid-world problem. You get a lot of knowledge from doing that. Then a bit more work on understanding various approaches, e.g policy learning, world models. Then, reading text books, blogs tutorials, etc.
But "getting" the idea of Q learning for a small state space is fundamental and surprisingly approachable.
I wrote this extensive tutorial for teaching deep reinforcement learning, with a focus on getting intuition from code. you will find RL theory is heavy on math despite needing math for very little other than abstractly representing some machine goal and intuition, of which code serves a native programmer already very well.
i spent years failing to learn machine learning and RL until i just started reading source code. books of integrals i never ended up needing.
dont be turned away by the joking nature of my tutorials. there is a real depth in there
+1 you beat me to the punch! I think its helpful to start with simple RL and ignore the "deep" part to get the basics. The first several lectures in this series do that well. It helped me build a simple "cat and mouse" RL simulation https://github.com/gtoubassi/SimpleReinforcementLearning and ultimately a reproduction of the DQN atari game playing agent: https://github.com/gtoubassi/dqn-atari.
I enjoyed the book Grokking Deep Reinforcement Learning from Manning. It's written in a very accessible style and explains the mathematical formulas you will see in other RL teaching material.
I'd suggest getting a good book or other teaching resource and solve a few Gymnasium[0] environments. Unlike supervised machine learning, you don't need someone else's data, you generate your own data.
But "getting" the idea of Q learning for a small state space is fundamental and surprisingly approachable.