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A Complete Reinforcement Learning System (Capstone)

The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end.By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or

3.8
6 reviews

Created by: Martha White

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Overall Score : 76 / 100

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Course Description

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.
Read More product length iconTime: 12h

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Instructor Details

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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.

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Reviews

3.8

6 reviews in total

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By Christos G on 14-Nov-19

Excellent course and specialization

By Prabhakaran on 13-Nov-19

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.

By Raviteja P on 9-Nov-19

Enjoyable.

By Divjyot on 9-Nov-19

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

By Guilherme A S M on 7-Nov-19

Not worth it.

By Dinesh A A on 7-Nov-19

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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A Complete Reinforcement Learning System (Capstone)
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  • Video Duration: icon 12h
  • Price: Free
  • Provider: icon Coursera
  • University: icon IBM
  • Language: icon English
  • Certificate: icon Yes

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