Reinforcement learning has a reputation for being both fascinating and brutally unforgiving, and CS 285 does not pretend otherwise. Levine builds the subject up in a deliberate order. Imitation learning first, because it is the simplest thing that works and its failure modes motivate everything after. Then policy gradients, actor critic methods, value based methods and the practical Q learning tricks that stop it from diverging.
Then the harder territory of model based RL, variational inference connections, offline RL, exploration, and more recently the application of RL to large language models. That last inclusion matters. A great deal of the current interest in this material comes from people who want to understand post training, preference optimisation and reward modelling, and having those topics taught by someone who has been doing the underlying research for a decade is worth a lot more than another blog post about it. The lectures are the main artefact, and they are excellent in a specific way.
Levine derives things. He will write the objective, take the gradient, show you where the variance comes from, and then show you the trick that reduces it. If you want intuition first and mathematics later, this will feel relentless. If you want to actually understand why an algorithm has the shape it has, this is the correct way to teach it and almost nobody else does it publicly at this level.
The homeworks are where learning happens. Implementing behaviour cloning, policy gradients, Q learning and offline methods yourself teaches you the thing that no lecture can convey, which is that reinforcement learning implementations fail silently. There is no exception thrown when your advantage estimate has the wrong sign. The agent simply does not improve, and you sit there deciding whether the bug is in your code, your hyperparameters, or your understanding.
Working through that is the actual skill, and it is why I would tell anyone using this course not to skip the assignments in favour of watching lectures. The barriers are real. This is a graduate course at a top department and it is priced accordingly in effort. You need comfort with probability, linear algebra and optimisation, and you need to have trained neural networks before.
Outside learners also lose the support structure, so there is nobody to tell you that your confusion in lecture nine is normal and resolves in lecture eleven. Study with someone if you can, because the dropout rate for solo attempts at this course is high. The last thing to be honest about is applicability. Deep RL is scientifically thrilling and practically narrow.
Outside robotics, control, games, and the increasingly important area of language model post training, most problems that look like RL are better solved with something simpler and more stable. That does not devalue the course, and it should shape your decision about whether to spend a semester on it. My 4.6 reflects material of exceptional quality with a small and self selecting audience. It is free, it is current, it is rigorous, and it will not meet you halfway.