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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Verstärkungslernen
von
Spezialisierung VerstärkungslernenUniversity of AlbertaÜber diesen Kurs
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Kompetenzen, die Sie erwerben
- 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.
Lehrplan - Was Sie in diesem Kurs lernen werden
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
Bewertungen
- 5 stars77,31 %
- 4 stars16,49 %
- 3 stars5,15 %
- 2 stars0,68 %
- 1 star0,34 %
Top-Bewertungen von A COMPLETE REINFORCEMENT LEARNING SYSTEM (CAPSTONE)
The comments given by the auto grader is not informative of the errors causing problem, and not sensitive enough to capture problems with action selection steps based on current state.
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.
Strongly recommend this course to others. The project could be a little more challenging though. Thanks, Martha, Adam, and RAs, for your good teaching!
I give 4 stars because this last course is not as good as the previous ones. No real complaints, but it's not as "complete".
Über den Spezialisierung Verstärkungslernen
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