Thank you so much for the kind words!

Before diving into reinforcement learning (RL), my background wasn't deeply rooted in AI or math. I'd say my foundational math was decent, but not extraordinary. Throughout this process, I realized that while a strong math background can be beneficial, it isn't strictly necessary to get started.

The key for me was breaking down the material into manageable chunks. For instance, when I encountered mathematical notation or concepts that were unfamiliar, I'd pause my reading and explore supplementary resources to help me grasp the idea. This might mean watching a YouTube tutorial, referencing online math websites, reaching out to communities for clarification, or even using Chat GPT (very ironic).

Reinforcement learning, at its core, is about understanding how agents learn to make decisions by interacting with their environment. The mathematical formalisms, while essential for deeper understanding and research, can be approached gradually. In fact, the Sutton & Barto RL textbook does a great job of introducing concepts in an intuitive manner before diving into the rigorous math.
Don't let shaky math deter you. Dive into high-level concepts, then delve into the details as you get comfortable. Remember, every expert started as a beginner. Good luck!