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A Complete Reinforcement Learning System (Capstone)

A Complete Reinforcement Learning System (Capstone)

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8.4/10 (Our Score)
Product is rated as #50 in category Artificial Intelligence

In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component –problem formulation, algorithm selection, parameter selection and representation design –fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: …

Instructor Details

Martha White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta, Faculty of Science. Her research focus is on developing algorithms for agents continually learning on streams of data, with an emphasis on representation learning and reinforcement learning. Martha is a PI of AMII -the Alberta Machine Intelligence Institute and a director of RLAI -the Reinforcement Learning and Artificial Intelligence Lab at the University of Alberta. She enjoys soccer, the outdoors, cooking and especially reading sci-fi.

Specification: A Complete Reinforcement Learning System (Capstone)

Duration

8 hours

Year

2019

Level

Intermediate

Certificate

Yes

Quizzes

No

27 reviews for A Complete Reinforcement Learning System (Capstone)

3.9 out of 5
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  1. Avatar

    Manuel V d S

    Not worth it.

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  2. Avatar

    Daniel M

    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.

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  3. Avatar

    Stewart A

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

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  4. Avatar

    Chang, W C

    Enjoyable.

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  5. Avatar

    David C

    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.

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  6. Avatar

    Andrew D G

    Excellent course and specialization

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  7. Avatar

    Mateusz K

    In my opinion, the capstone should’ve included more development and or programming. I liked having to develop NN action value function approximator, but the parameter study was a bit too simple (should’ve had more code content).

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  8. Avatar

    koji t

    This course was the best course for me as a beginner in reinforcement learning.

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  9. Avatar

    Yichen W

    The comments given by the auto grader is not informative of the errors causing problem, and not sensitive enough to capture problems with action selection steps based on current state.

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  10. Avatar

    Justin S

    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!

    Helpful(2) Unhelpful(0)You have already voted this
  11. Avatar

    Akash B

    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.

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  12. Avatar

    Ivan S F

    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.

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  13. Avatar

    Umut Z

    Could be more detailed in the environment setup

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  14. Avatar

    Qiuping X

    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.

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  15. Avatar

    allonhammer

    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

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  16. Avatar

    David R

    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.

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  17. Avatar

    A4

    awesome

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  18. Avatar

    Alberto H

    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!

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  19. Avatar

    Dmitry S

    Good course. Summarises and puts everything in context. But would benefit from having larger programming assignments (which would make it more challenging as well) when less things are provided out of the box, and from a bit more extended and systematic overview and walk through of the material.

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  20. Avatar

    Kayla S

    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.

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  21. Avatar

    Walter O A

    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.

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  22. Avatar

    Antonio P

    Good course

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  23. Avatar

    Lik M C

    The project is interesting. But the implementation left as assignments is too simple. There are too many guidance running in assignments. If more flexibility is allowed in implementing the project, it should be even more interesting.

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  24. Avatar

    Maxim V

    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.”

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  25. Avatar

    naoufel

    I would have expected to do more of the implementation in the capstone project. It is still quite useful to go through the details of what is implemented by the instructor for us, but not as effective as having to implement more of it ourselves. Thanks

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  26. Avatar

    Mohamed S R I

    Thanks a lot for offering this specialization! I really enjoyed watching the videos and working on the assignments while exploring various topics of RL.

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  27. Avatar

    Roberto M

    The project is well structured and very helpful to connect all the dots

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    A Complete Reinforcement Learning System (Capstone)
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