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アルバータ大学(University of Alberta) による Sample-based Learning Methods の受講者のレビューおよびフィードバック

4.8
193件の評価
41件のレビュー

コースについて

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna...

人気のレビュー

KN

Oct 03, 2019

Great course! The notebooks are a perfect level of difficulty for someone learning RL for the first time. Thanks Martha and Adam for all your work on this!! Great content!!

UZ

Nov 23, 2019

Good balance of theory and programming assignments. I really like the weekly bonus videos with professors and developers. Recommend to everyone.

フィルター:

Sample-based Learning Methods: 1 - 25 / 40 レビュー

by Manuel V d S

Oct 04, 2019

Course was amazing until I reached the final assignment. What a terrible way to grade the notebook part. Also, nobody around in the forums to help... I would still recommend this to anyone interested, unless you have no intention of doing the weekly readings.

by Kaiwen Y

Oct 02, 2019

I spend 1 hour learning the material and coding the assignment while 8 hours trying to debug it so that the grader will not complain. The grader sometimes insists on a particular order of the coding which does not really matter in the real world. Also, grader inconsistently gives 0 marks to a particular part of the problem while give a full mark on other part using the same function. (Like numpy.max) However, the forum is quite helpful and the staff is generally responsive.

by LuSheng Y

Sep 10, 2019

Very good.

by Luiz C

Sep 13, 2019

Great Course. Every aspect top notch

by Ashish S

Sep 16, 2019

A good course with proper Mathematical insights

by Sohail

Oct 07, 2019

Fantastic!

by koji t

Oct 07, 2019

I made a lot of mistakes, but I learned a lot because of that.

It ’s a wonderful course.

by Alejandro D

Sep 19, 2019

Excellent content and delivery.

by Sodagreenmario

Sep 18, 2019

Great course, but there are still some little bugs that can be fixed in notebook assignments.

by Stewart A

Sep 03, 2019

Great course! Lots of hands-on RL algorithms. I'm looking forward to the next course in the specialization.

by Mark J

Sep 23, 2019

In my opinion, this course strikes a comfortable balance between theory and practice. It is, essentially, a walk-through of the textbook by Sutton and Barto entitled, appropriately enough, 'Reinforcement Learning'. Sutton's appearances in some of the videos are an added treat.

by Ivan S F

Sep 29, 2019

Great course. Clear, concise, practical. Right amount of programming. Right amount of tests of conceptual knowledge. Almost perfect course.

by Damian K

Oct 05, 2019

Great balance between theory and demonstration of how all techniques works. Exercises are prepared so it is possible to focus on core part of concepts. And if you will you can take deep dive into exercise and how experiments are designed. Very recommended course.

by Kyle N

Oct 03, 2019

Great course! The notebooks are a perfect level of difficulty for someone learning RL for the first time. Thanks Martha and Adam for all your work on this!! Great content!!

by Rashid P

Nov 12, 2019

Best RL course ever done

by Alex E

Nov 19, 2019

A fun an interesting course. Keep up the great work!

by LUIS M G M

Nov 22, 2019

Great course!!! Even better than the 1st one. I tried to read the book before taking the course, and some algorithmics have not been clear to me until I saw the videos (DynaQ, DynaQ+). Same wrt some key concepts (on vs off policy learning).

by Umut Z

Nov 23, 2019

Good balance of theory and programming assignments. I really like the weekly bonus videos with professors and developers. Recommend to everyone.

by Nikhil G

Nov 25, 2019

Excellent course companion to the textbook, clarifies many of the vague topics and gives good tests to ensure understanding

by Li W

Nov 27, 2019

Very good introductions and practices to the classic RL algorithms

by Manuel B

Nov 28, 2019

Great course! Really powerful but simply ideas to solve sequential optimization problems based on learning how the environment works.

by AhmadrezaSheibanirad

Nov 10, 2019

This course doesn't cover all concept of Sutton book. like n-step TD (chapter7) or some Planning and Learning with Tabular Methods (8-5, 8-6, 8-7, 8-8, 8-9, 8-10, 8-11), but what they teach you and cover are so practical, complete and clear.

by Shi Y

Nov 10, 2019

最喜欢的Coursera课程之一,难度适中的RL课程,非常推荐,学习到了很多自学很难理解全面的知识。感谢老师和助教们!

by John H

Nov 10, 2019

It was good.

by Alberto H

Oct 28, 2019

A great step towards the acquisition of basic and medium complexity RL concepts with a nice balance between theory and practice, similar to the first one.

[Note: the course requires mastering the concepts of the first one in the specialization, so don't start here unless you're sure you master its contents.]