Reinforcement Learning is one of the most interesting topics in computer science and ML. I started with the canonical textbook — Reinforcement Learning: An Introduction by Sutton & Barto — which is a fantastic read, though some topics took multiple passes to properly absorb.
After working through Dynamic Programming, Monte-Carlo methods, and Temporal Difference learning, I wanted practical experience and turned to Coursera’s Practical Reinforcement Learning course.
Honest review: it was frustrating. The course tried to cover too much ground too quickly, assignment instructions were minimal and error feedback sparse, and by the halfway point I was no longer enjoying it. I pushed through to the end.
That said, the material itself — not the course’s delivery of it — remains genuinely fascinating. I hope to apply it professionally at some point.