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.
Syllabus - What you will learn from this course
Welcome to the Final Capstone Course!
Milestone 1: Formalize Word Problem as MDP
Milestone 2: Choosing The Right Algorithm
Milestone 3: Identify Key Performance Parameters
Reviews
- 5 stars77.26%
- 4 stars16.58%
- 3 stars5.12%
- 2 stars0.51%
- 1 star0.51%
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.
Matha and Adam, thank you again. I will try to apply what I learned here to my own work, a content recommendation system based on deep learning and reinforcement learning.
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.
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.
About the Reinforcement Learning Specialization
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