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Online Course

A Complete Reinforcement Learning System (Capstone)

University of Alberta and Alberta Machine Intelligence Institute via Coursera

Machine Learning & AI Diploma - 9 Months, Online Columbia Engineering Executive Education via EMERITUS AD
  • Provider Coursera
  • Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Duration 6 weeks long
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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.


Welcome to the Final Capstone Course!
-Welcome to the final capstone course of the Reinforcement Learning Specialization!!

Milestone 1: Formalize Word Problem as MDP
-This week you will read a description of a problem, and translate it into an MDP. You will complete skeleton code for this environment, to obtain a complete MDP for use in this capstone project.

Milestone 2: Choosing The Right Algorithm
-This week you will select from three algorithms, to learn a policy for the environment. You will reflect on and discuss the appropriateness of each algorithm for this environment.

Milestone 3: Identify Key Performance Parameters
-This week you will identify key parameters that affect the performance of your agent. The goal is to understand the space of options, to later enable you to choose which parameter you will investigate in-depth for your agent.

Milestone 4: Implement Your Agent
-This week, you will implement your agent using Expected Sarsa or Q-learning with RMSProp and Neural Networks. To use NNs, you will have to use a more careful stepsize selection strategy, which is why you will use RMSProp. You will also verify the correctness of your agent.

Milestone 5: Submit Your Parameter Study!
-This week you will identify a parameter to study, for your agent. Once you select the parameter to study, we will provide you with a range of values and specific values for other parameters. You will write a script to run your agent and environment on the set of parameters, to determine performance across these parameters. You will gain insight into the impact of parameters on agent performance. You will also get to visualize the agents that you learn. Your parameter study will consist of an array of values that we will check for correctness.

Taught by

Martha White and Adam White

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Reviews for Coursera's A Complete Reinforcement Learning System (Capstone) Based on 5 reviews

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Anonymous completed this course.
This was my least-favorite of the RL Specialization on Coursera. Most of the lectures were repeats, and the programming assignments were either way too easy (#1 and #3) or way too involved/difficult (#2).

However, I did love the idea of pulling all the knowledge together into a single course with a unified project. The "Meeting with..." videos and the guest lectures from this class were definitely some of my favorite videos/lectures out of the entire specialization. I just wish there were more of them!
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Anonymous completed this course.
Nice course, it contains some insights I wish I saw in the first course of the specialization.

It has the same defaults as the other course of the specialization: the notebooks have to fit exactly the ones created by the team, it lacks some creativity in the approaches, by asking us to fill the holes in algorithms, but it's nice to see the whole implementation behind an RL algorithm in practice.

I recommend the whole specialization as an introduction to RL.
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Anonymous completed this course.
This course was a great finale to the Reinforcement Learning Specialization. The coding segments were challenging but completable, and the lectures were clear and informative. I really liked how they included several mini-lectures by professionals in the field of machine learning. They were not required to complete the course, but they were super insightful.
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Anonymous completed this course.
This is a great course and the best wrap up for the Reinforcement Learning Specialization. It covers in the whole thought process and aspects involved in creating an RL Agent.
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Anonymous completed this course.
The course material was well done and very interesting. The programming assignments were very helpful in fully understanding the concepts behind the different algorithms
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