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.
Este curso forma parte de Programa especializado: Aprendizaje por refuerzo
ofrecido por
Programa especializado: Aprendizaje por refuerzoUniversidad de AlbertaAcerca de este Curso
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Habilidades que obtendrás
- 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.
Programa - Qué aprenderás en este curso
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
Reseñas
- 5 stars77,35 %
- 4 stars16,46 %
- 3 stars5,14 %
- 2 stars0,68 %
- 1 star0,34 %
Principales reseñas sobre A COMPLETE REINFORCEMENT LEARNING SYSTEM (CAPSTONE)
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.
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.
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.
Short but useful course to learn all the details about the implementation side of the concepts learned along the Specialization.
Acerca de Programa especializado: Aprendizaje por refuerzo
Preguntas Frecuentes
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