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Learner Reviews & Feedback for A Complete Reinforcement Learning System (Capstone) by University of Alberta

4.7
stars
648 ratings

About the Course

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: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution....

Top reviews

GC

Apr 3, 2020

The course is applicative in real world projects. I think it is a very good choice for any one that is interested to learn how to apply reinforcement learning.

HH

Jan 12, 2022

It may have been useful to provide less guidance to the students to make sure they develop the required skills. Overall, it was a nice exercise to implement a TD(0) network.

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126 - 130 of 130 Reviews for A Complete Reinforcement Learning System (Capstone)

By Tianpei X

•

Aug 15, 2022

good practice of RL in a simulated example

By Antonio P

•

Jan 21, 2020

Good course

By Naoufel E B

•

Feb 16, 2020

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

By Dustin Z

•

May 5, 2021

A good review of the material covered in the specialization. I would have liked to see a more thorough parameter study and deeper coverage of the environment wrapper around the OpenAI lunar lander environment.

By Manuel V

•

Nov 7, 2019

Not worth it.