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学生对 阿尔伯塔大学 提供的 A Complete Reinforcement Learning System (Capstone) 的评价和反馈

4.7
561 个评分
118 条评论

课程概述

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....

热门审阅

JJ

Apr 27, 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

Feb 26, 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.

筛选依据:

76 - A Complete Reinforcement Learning System (Capstone) 的 100 个评论(共 120 个)

创建者 Fintan K

Nov 24, 2020

Brilliant Course All Round!

创建者 RICARDO A F S

Nov 22, 2020

Let's go to the moon!

创建者 Ryan Y

Mar 4, 2021

Thank you very much!

创建者 dariojavo

Oct 18, 2020

Excellent material!

创建者 Jose

Jun 29, 2020

excellent course

创建者 BC

May 6, 2020

Excellent course

创建者 RUI D

Aug 2, 2021

Nice Course!

创建者 Yanlin L

Apr 19, 2020

GOOGD GOOD

创建者 Chang, W C

Nov 8, 2019

Enjoyable.

创建者 남상혁

Jan 18, 2021

Very good

创建者 Tran M D

May 22, 2020

Excellent

创建者 A4

Jan 1, 2020

awesome~

创建者 ARTEM B

Mar 1, 2021

Great!

创建者 Justin O

May 22, 2021

Great

创建者 Adrian Y X

Apr 4, 2020

I will write a longer review for the entire Specialization later, but this course does well to sum up all of the other progress you've had made thus far on the Specialization. However, you'll find that from Course 2 onwards (and this one especially), very little hand holding is given for the programming assignments. Command of numpy and python at good level are expected. Personally, having worked with OpenAI gyms before starting this specialization helped me immensely. As the instructors state, this course lays the foundation for future studies. The field of RL is simply so complex that even foundational work is challenging. Overall, a great course.

创建者 Steven W

May 11, 2021

They mostly discuss the importance of real world experience and hyperparameter tuning in this class. The content it did have was solid and the instructors were great. The "capstone" was creating an agent to solve the Moon Lander problem, and much of the code was already written.

I would have really preferred getting experience with a real RL framework like RLLib or acme, rather than the toy libraries used by the book. It would have also been really nice to have a little more freedom and challenge, such as making us actually create an agent to solve an MDP of our own choosing and definition.

创建者 Henry C

Oct 16, 2021

A decent course to wrap up the RL specialization, with a "project" that demonstrates a "real-world" application of RL.

The word "project" is in quotes because it is structured as a (short) series of fairly short assignments with very heavy hand-holding, so very similar to previous courses.

My only complaints with this course are that the project is a bit too hand-holdy and that the course overall is quite short and thin in content. I would estimate that this course is around 1/3 the length of the previous courses in this series.

创建者 Jing Z

Jun 2, 2020

The project is a decent example to go through in order to review what we learned from previous courses. However there are few key things supposed to be addressed as well: 1) What exactly the reward function is in the final project (C4M1 practice is badly designed); 2) How can we build an environment on our own; 3) Apart from Mean Squared Value Error to be minimized, what are other loss functions to choose from and what's the consideration behind.

创建者 Francisco M

Jul 12, 2022

I am a recently junior researcher in the Optimization field, approaching predictive and prescriptive online retail problems. Therefore, I truly believe this complete reinforcement learning specialization gave me the foundations to evolve my research in this domain. About the structure and contents of the specialization, I think it is very well organized in the 4 main courses. Thanks to the team.

创建者 Dmitry S

Jan 10, 2020

Good course. Summarises and puts everything in context. But would benefit from having larger programming assignments (which would make it more challenging as well) when less things are provided out of the box, and from a bit more extended and systematic overview and walk-through of the material.

创建者 Ahmed S S A

Mar 5, 2020

Great course, thanks a lot really. But I do hope if we did visualize the environment to see how my agent behaves and then saves the RL agent to use it offline after being trained. Really thank you so much for making RL clear to me and interesting too :) <3

创建者 Alaaeldin Z

May 24, 2021

I liked the project. I hoped it would be harder and enable the students to design the whole agent and environment code and be evaluated with a human grader. But overall, I was able to practice the concepts I have learnt throughout the specialization.

创建者 Surya K

May 3, 2020

A cherry on top of the cake. This course helped me understand how to think about a novel problem and formulate and build an RL system from scratch. I thank Course Instructors, University of Alberta, and Coursera for this beautiful specialization.

创建者 Lik M C

Jan 23, 2020

The project is interesting. But the implementation left as assignments is too simple. There are too many guidance running in assignments. If more flexibility is allowed in implementing the project, it should be even more interesting.

创建者 Mateusz K

Nov 16, 2019

In my opinion, the capstone should've included more development and or programming. I liked having to develop NN action-value function approximator, but the parameter study was a bit too simple (should've had more code content).