Just finished Lecture 4 of David Silver’s course. Should I pause to implement or push through the theory?

I’ve just started learning Reinforcement Learning and finished watching Lecture 4 (Model-Free Prediction) of David Silver’s course.

I’m loving the theory and most concepts are clicking (MDPs, Bellman equations), though I sometimes have to pause to check Sutton & Barto when the math gets dense. However, I realized today that I haven’t actually written a single line of code yet.

I’m comfortable with general ML and math, but completely new to RL practice.

Two questions for those who have gone down this path:

1. Is it better to pause right now and implement the basics to solidify the concepts,
2. should I finish the full playlist to get the “big picture” first?

Can you guys provide me with resources to practically align with the David silver’s playlist.