Stanford has quietly become the default source for serious, freely available computer science courses in this field, and CS234 is its dedicated reinforcement learning offering, sitting naturally next to the machine learning, deep learning, NLP and computer vision courses that people already point newcomers toward. Taught by Emma Brunskill, who works in the area, it takes reinforcement learning seriously as a subject in its own right and builds it up properly, starting from the foundations of Markov decision processes, value functions and the core algorithms, moving through policy gradients and exploration, and on into the modern deep reinforcement learning methods that connect the classical theory to today's practice. That coherent arc is the main reason to choose it, because reinforcement learning is an area where shallow tutorials leave you able to run someone else's code without any real grasp of why it works or when it will fail, and this course is built to close exactly that gap. The assignments are a big part of the value, since they push you to actually implement the ideas rather than nod along to them, and doing them is what turns watching the lectures into genuine understanding.
The honest caveats are the same ones that come with any real university course. The prerequisites are steep, and you will struggle badly without a comfortable grounding in probability, linear algebra and Python, so this is emphatically not a first course for someone new to the field, it is a course for someone with the machine learning basics already in place who now wants depth in RL specifically. Reinforcement learning is also just intrinsically difficult, arguably harder to build intuition for than supervised learning, and the course reflects that reality rather than smoothing it over, so the learning curve is real and you should expect to work. As with most academic material, the production is functional rather than glossy, these are recorded university lectures with slides, not a highly polished commercial course, and if you follow it freely rather than enrolling for credit you get no grading, no deadlines and no support, so the discipline has to come from you.
My take is that CS234 is one of the best ways to learn reinforcement learning properly and for free, but only once you are ready for it. If you already have the fundamentals and you want to move from being able to call an RL library to actually understanding the algorithms well enough to reason about and implement them, this is an excellent and rigorous route. If you are still early in your journey, get your machine learning and mathematics foundations solid first, then come back to this when you can give it the effort it demands and genuinely deserves.