Avis et commentaires pour d'étudiants pour A Complete Reinforcement Learning System (Capstone) par Université de l'Alberta

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.

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!

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.

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!

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.

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.

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."

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.

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

I like the course lectures, and those are great explanation and additional to the Sutton's book. The deduction of the two stars are primarily for the quiz and coding assignments. Most of the time, the quiz is not clear and the coding assignment is confusing too, and not very well structured.

This is my overall review for all the courses in this specialization. In my opinion, this specialization can be a good supplement to the RL textbook. There are some instances where the lecture video can describe certain content better than the textbook. One should also remember that the depth covered by this specialization is much less compared to the textbook, therefore one is still strongly encouraged to read the textbook thoroughly to have a better understanding of the topics. Other good things to be said about this specialization is that the Jupyter notebook exercises are rather well-prepared. However, the last course (Capstone) was actually surprisingly easy, so although the course estimates 6 weeks worth of content, I feel it's more towards 1-2 weeks (could be even less if you skip the review lessons which are duplicates of videos in previous courses). Throughout the courses there are also guest lecture videos. Most of them are interesting enough, although for some the content may be too far from course content (perhaps even textbook). Overall this specialization is definitely a good place to start learning reinforcement learning!

The same review for all 4 courses: This is the first time I have done a Coursera module building courses up rather than just individual courses. You really feel you have achieved something out of it. Some people have commented that it is just presenting material from the Sutton and Barto book. But that book is *the* text book in the subject. The course selected particular chapters from the book. I wouldn't have got as much just from trying to read the book on its own (I probably wouldn't have read as much as I did). It was good to have the supplementary videos with other experts - and great to watch Sutton and Barto just sat down being recorded having a retrospective conversation. The programming exercises would sometimes feel they weren't testing much (in fact, the challenges were largely due to my lack of skill in Python - my Python abilities have improved which is a side benefit) but they would actually get you into the weeds as it were. All in all, the best courses I've done. Great job Martha and Adam!

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.

Thank you every one (onscreen and offscreen) who built this amazing course. I am a robotics and automation engineer. I learned reinforcement learning from a 20 hour youtube lecture of David Silver from deepmind. Despite that being a great course I joined this course to make my foundation concrete. And to my surprise the presentation of complex concepts in this course was remarkably good. Every ideas were presented in a very simplified manner. Thanks team. This course is highly recommended to anyone, including absolute beginners, wanting to learn reinforcement learning.

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 course, and the whole specialization, have been transforming for my career, as I am willing to pursue a PhD on reinforcement learning as soon as I graduate. I am very thankful that this course exists, because it got me to understand from the fundamentals of the area to advanced topics in a digestible, well-structured, with lots of material to cover and challenges to overcome. Thank you so much!

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.

The instructors are simply amazing! They cover a lot of algorithms, highlighting their motivations, use cases, and limitations. What fascinated me is that all the different topics covered are based on very simple, yet powerful ideas. Because of this course, I feel equipped with knowledge and experience to start working on my own projects and consider pursuing RL and AI for my graduate studies.

Specialisation is really worth it. Especially it is really great introduction for beginners in RL. Martha is great instructor, all her lessons were really clear for me and easy to understand. Adam's lectures as for me require more time and effort to catch sense, but still really good. Quality of every course in that specialisation is higher than average on coursera I definitely recommend it.

Excellent course and specialisation for those who wish to understand the basics of reinforcement learning. A must complete specialization to those who are interested in the Reinforcement Learning: An Introduction by Sutton and Barto as it greatly enhanced my understanding of the content with videos, quizzes, and programming in addition to simplified examples of the core concepts.

It goes without saying, this was a fantastic introduction to reinforcement learning. There is so much to learn though, there is room for the authors (or any other author) for the creation of more specialized classes, or conceptually centric ones (why does RL work at all). In short, hats off!