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.
This course is part of the Reinforcement Learning Specialization
Offered By
Reinforcement Learning SpecializationUniversity of AlbertaAbout this Course
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Skills you will gain
- Artificial Intelligence (AI)
- Machine Learning
- Reinforcement Learning
- Function Approximation
- Intelligent Systems
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Offered by
University of Alberta
UAlberta is considered among the world’s leading public research- and teaching-intensive universities. As one of Canada’s top universities, we’re known for excellence across the humanities, sciences, creative arts, business, engineering and health sciences.
Alberta Machine Intelligence Institute
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
Syllabus - What you will learn from this course
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.
Reviews
- 5 stars77.44%
- 4 stars16.60%
- 3 stars5.06%
- 2 stars0.52%
- 1 star0.34%
TOP REVIEWS FROM A COMPLETE REINFORCEMENT LEARNING SYSTEM (CAPSTONE)
Great course for learning the fundamentals. I liked that it tied into function approximation for deep reinforcement learning. The text book made the fundamental concepts more clear.
The project seems to be complicated at first glance, but the notebook will guide you through the implementation, and you will know what you are doing eventually.
Project could be better designed and could be made more fun. The first 3 courses were brilliant. I finished the entire capstone in less than 26-hours to save money!
One of the most amazing set of courses that I have ever been through. This neither makes the stuff look difficult nor does it compromise on quality, absolutely the best.
About the Reinforcement Learning Specialization
The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).
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