A great course/specialization, and the one in reinforcement learning you were looking for. A lot of work has been put into creating this specialization. Maybe a bit less into this last course (capstone) which consists of a patchwork of lectures from previous courses and some new ones. The capstone project is not fundamentally different from assignments in previous courses. Be aware, even if you’ve made it through the whole specialization it most likely doesn’t mean that you will be ready to return to your own area of interest/expertise and implement an RL project from scratch. Still, I would highly recommend taking the full specialization if you meet the programming prerequisites.

I really liked the new videos ("Meeting with...") and the idea of using all the information learned through the other courses to tackle a project. However, this course seems to not be fully thought-through. I didn't love the re-inclusion of videos I had already seen in the past (which were sometimes only tangentially-related to that week's topic). The programming assignments were either way too easy (#1 and #3) or way too difficult/involved/long (#2). The pacing of this course was way off as well, I don't think it should be broken into 6 weeks. I finished the entire thing in about 1 week.

This course changed my life! It was so good and I learned so much. I can't believe I'm now an astronaut. Next mission: go to Mars!

You might, like me, have acquired some understanding on several areas of RL (Q-learning, Policy Gradient...) from available sources (selected papers, articles, blogs, tutorials...), and were waiting for "the right" course to come up, wrapping up all existing and missing bits into one solid foundation.

If that's your case, don't waste any more time or money somewhere else: this course is the course you are needing. It will take you step by step (always) from the basics of bandits to MDP solutions and from tabular algorithms to more sophisticated function approximation algorithms.

And if you're just starting to scratch on this great field... well, I don't think you'll currently find a better online course, and I've seen quite a few.

Thanks for putting this together, Martha and Adam!

Very good course. Compared to the prior courses in the specialization, it appears to be still a course under development rather than a final product. I recommend that the instructors work more on this course (the other courses in the specialization are very very good).

Keep up the great work.

Very good lectures - very informative and on point when it comes to theory but lacking in actual application of the theory. However, the projects are TERRIBLE. They could actually be very good, but there is simply not enough information in the descriptions to be very useful. None of the lectures discuss the details of how to implement any of the topics and the projects basically set things up but provide no information on what is actually expected to be done. They need to either discuss the basics or provide pointers to resources that provide that description. Some of the forums are helpful in clarifying things, but the projects really need someone knowledgeable in this area to rework things extensively.

Unlike the previous courses in this specialization, this course seems a bit unripe. There's very little material added here (perhaps the only thing new is the Replay Experience algorithm, which is introduced rather briefly). It's more like a general recap of the previous 3 courses. I kind of hoped for something more challenging and broad - but the scope here was rather limited.

It is clear that a lot of effort has been put in this course. Excellent examples and very clear explanations of the theoretical material. The down side is the programming assignment is too easy, and we didn't actually implement the environment

Good content, but considering the bugginess of graders and the necessity to submit results separately from notebook, this requirement is too extreme: "Retakes: You can attempt this assignment 5 times every 4 months."

Excellent final course for the specialization. Moon Lander project was informative and fun.

The capstone project was very well chosen and it was a fascinating problem to solve. The professors explained a complete workflow to conduct towards a scientific experiment in order to solve the problem efficiently. It was good to review some of the concepts and algorithms from the previous courses in the specialization to have a bigger picture of the path we went through. In addition, I had a great time watching the videos with other professors and special guests such as the one with David Silver and Joelle Pineau. Finally, I really appreciate the effort that Mr. and Mrs. White made to make this specialization available in Coursera and to share their knowledge and experience. I believe that I have a good foundation in Reinforcement Learning now and will continue the reading of the remaining chapter of the text book.

If you are getting into RL, I highly recommend going straight into this specialization. This course is an absolute gold and so is the accompanying book - "The Reinforcement Learning" by Sutton and Barto. The problem with sub-concepts in RL are very subtle and looks very similar and there is pretty good chance you end with confusion. This is where this specialization shines - building each concepts incrementally to give you the bigger picture. I am now so much confident with RL and know where each concept/algorithm fits. Thank you so much for this Specialization.

This course ties everything in the previous three courses together to simulate a reinforcement learning system for landing a lunar module on the Moon. The programming assignments are more or less guided, as in the previous courses, and the capstone project doesn't take very long to finish, despite the nominal 6-week length. The end result is fun and satisfying. Overall, I would highly recommend this specialization to anyone who is curious about reinforcement learning methods in machine learning.

Absolute fantastic!! Thank you to everyone that put this course together. I loved the "behind the scenes" decision making on choices of approaches - I just wish there were some more them to "distinguish" the process for making different choices. The instructors are excellent teachers. Would love the opportunity to sit in a class live and interact and ask questions!! Was great fun digging into the code and understanding the data structures.

This capstone project is really amazing as how it gives the overall expertise understanding for experimentation and how to implement the algorithm. From MDP to scientific selection of meta-parameters are really important to decide about how should be make an agent, but there are lots of considerations.Overall, this was a great experience and would remember the instructors for all my life. Thanks.

A solid introduction to the subject matter of Reinforcement Learning. Especially helpful navigating through the Sutton & Barto book. The programming labs all worked and included robust tests for correctness. I especially appreciated this as I have spent significant time in other courses banging my head on the wall because of an incorrect or vague lab assignment. Well worth the time invested.

This course sums up all the fundamental knowledge acquired from the previous 3 courses. It covers comprehensively on a sample end-to-end RL problem think process, application and assessment on the performance of model. It helped me better visualize how RL can be applied.

Would be great if we can see the video of our agent instead of a pre-recorded video. Also, the checking is not perfect and inconsistent. A great course and I learned a lot in RL. Thanks Martha and Adam! Wish you all the best!

Best course I have taken on Coursera so far. A great mathematically oriented introduction to RL techniques, ending with the modern state of the field. I would reccomend this course to anyone interested in RL!

This is the final chapter. It is one of the easiest and it was fun doing that lunar landing project. This specialisation is the best for a person taking baby steps in the reinforcement learning.

Great course for learning the fundamentals. I liked that it tied into function approximation for deep reinforcement learning. The text book made the fundamental concepts more clear.

Loved this entire course. Learned a lot. In a relatively easy way.

Lots of fun of as well. Indeed, I have already used some of these new skills in the real world.

Couldn't be better!

Matha and Adam, thank you again. I will try to apply what I learned here to my own work, a content recommendation system based on deep learning and reinforcement learning.

One of the most amazing set of courses that I have ever been through. This neither makes the stuff look difficult nor does it compromise on quality, absolutely the best.

The project seems to be complicated at first glance, but the notebook will guide you through the implementation, and you will know what you are doing eventually.