Depends how you like to learn. I started with a Udacity course: https://classroom.udacity.com/courses/ud600 and went on to read Reinforcement Learning: An Introduction. For me that has been a theory-heavy approach and I only recently wrote a TicTacToe bot (using Q-learning). However, when I did get around to writing it, I was very confident about what I was doing and why.
The starting point in OpenAI could be to follow a tutorial on one of the other simple environments (e.g. CartPole) - of which there are many online, and once you understand that, try changing the environment to TicTacToe and adapting the code to learn that.
At some point you will need to take a detour from “pure” reinforcement learning and learn at least a little supervised machine learning. Andrew Ng’s course on Coursera might be a good start for that.
You may want to brush up on basic stats, calculus and linear algebra (matrices, vectors etc). You don’t need to know them in great depth, but the basics are essential to understanding the theory and taking things further - both in reinforcement learning and supervised learning.