Chevron Left
Volver a A Complete Reinforcement Learning System (Capstone)

Opiniones y comentarios de aprendices correspondientes a A Complete Reinforcement Learning System (Capstone) por parte de Universidad de Alberta

4.7
estrellas
566 calificaciones

Acerca del Curso

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. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution....

Principales reseñas

JJ

27 de abr. de 2020

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.

CR

26 de feb. de 2020

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.

Filtrar por:

1 - 25 de 123 revisiones para A Complete Reinforcement Learning System (Capstone)

por Daniel M

7 de nov. de 2019

por Justin S

6 de dic. de 2019

por Kayla S

13 de ene. de 2020

por Alberto H

4 de ene. de 2020

por D. R

2 de ene. de 2020

por Ivan S F

14 de dic. de 2019

por David C

13 de nov. de 2019

por אלון ה

29 de dic. de 2019

por Maxim V

25 de ene. de 2020

por Neil H

10 de nov. de 2021

por Alireza M

10 de dic. de 2021

por Stewart A

9 de nov. de 2019

por Qiuping X

24 de dic. de 2019

por Connor W

1 de abr. de 2021

por Maximiliano B

26 de abr. de 2020

por Mohammed A N

29 de sep. de 2020

por Niraj S

23 de may. de 2020

por Jesse W

29 de jul. de 2020

por Mukund C

2 de abr. de 2020

por César S

28 de sep. de 2021

por Akash B

8 de dic. de 2019

por Walter O A

18 de ene. de 2020

por Varun B

20 de sep. de 2020

por Pavel I

27 de jul. de 2021

por Dale G

2 de ago. de 2021