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 faz parte do Programa de cursos integrados Reforço de aprendizagem
oferecido por
Programa de cursos integrados Reforço de aprendizagemUniversidade de AlbertaUniversidade de AlbertaInformações sobre o curso
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Habilidades que você terá
- 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.
oferecido por
Universidade de AlbertaUniversidade de 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
Programa - O que você aprenderá com este curso
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.
Avaliações
- 5 stars77,31%
- 4 stars16,75%
- 3 stars5,06%
- 2 stars0,52%
- 1 star0,34%
Principais avaliações do A COMPLETE REINFORCEMENT LEARNING SYSTEM (CAPSTONE)
This is the final chapter. It is one of the easiest and it was fun doing that lunar landing project. This specialisation is the best for a person taking baby steps in the 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!
Great Course. But, it would be much more fun if the programming assignments were implemented in for instance tensorflow or pytorch!
The course is well constructed guiding you till the end of the project. Maybe it would be funny to see our own agent failing and learning.
Sobre Programa de cursos integrados Reforço de aprendizagem
The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).
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