Reinforcement learning is the corner of machine learning where people most often skip the foundations and pay for it later, and this Alberta specialization is the antidote. It is a four-course path from the University of Alberta and the Alberta Machine Intelligence Institute, taught by Adam and Martha White, and it is built directly on Sutton and Barto's Reinforcement Learning: An Introduction, which is the canonical text the whole field still points to. That pairing of the standard textbook with instructors who actively research the subject is the reason I rate it so highly. The sequencing is excellent, starting with the fundamentals of value functions and the Markov decision process framing, moving through sample-based methods like Monte Carlo and temporal-difference learning, then into prediction and control with function approximation where the neural networks finally enter, and closing with a capstone where you assemble a complete reinforcement learning system.
By the time you finish you understand the space of algorithms, Sarsa, Q-learning, policy gradients, Dyna and the rest, not as names but as tools you know when to reach for. The honest caveats are about who it suits. This expects you to arrive comfortable with probability, calculus, and Python, and it does not slow down to rebuild those, so it is squarely aimed at people with at least a year of computer science behind them or a few years of development experience. It is also, deliberately, a foundations course.
It will make you genuinely understand reinforcement learning, but it is lighter on the very latest deep RL algorithms and the engineering of training large agents, so if your mental image of RL is purely the modern deep variety you should know the emphasis is on the bedrock rather than the frontier. As with everything on Coursera, the certificate is a Coursera specialization credential signed by the instructors rather than University of Alberta course credit, and you can audit the material for free if you do not need the certificate, paying only if you want graded assignments and the badge. My take is that if you want to truly understand reinforcement learning, this is the path I would point you to first, and I would pair it with the Sutton and Barto book open alongside the videos. If your actual need is narrower, fine-tuning a model with RLHF or running an off-the-shelf agent, then this is more theory than your goal demands, and you would be better served by a focused practical resource.
For the person who wants the real thing, though, it is hard to beat.