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Отзывы учащихся о курсе Sample-based Learning Methods от партнера Альбертский университет

4.8
Оценки: 72
Рецензии: 21

О курсе

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...

Лучшие рецензии

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!!

IF

Sep 29, 2019

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

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1–24 из 24 отзывов о курсе Sample-based Learning Methods

автор: Yanick P

Sep 17, 2019

Course material for next course is not available but Coursera still charge me $$

автор: 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.

автор: 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.

автор: LuSheng Y

Sep 10, 2019

Very good.

автор: Luiz C

Sep 13, 2019

Great Course. Every aspect top notch

автор: Alejandro D

Sep 19, 2019

Excellent content and delivery.

автор: Sodagreenmario

Sep 18, 2019

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

автор: Stewart A

Sep 03, 2019

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

автор: 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.

автор: 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.

автор: 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.

автор: 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!!

автор: Ashish S

Sep 16, 2019

A good course with proper Mathematical insights

автор: koji t

Oct 07, 2019

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

It ’s a wonderful course.

автор: Sohail

Oct 07, 2019

Fantastic!

автор: Ignacio O

Oct 13, 2019

Great, informative and very interesting course.

автор: Wang G

Oct 19, 2019

Very Nice Explanation and Assignment! Look forward the next 2 courses in this specialization!

автор: Sriram R

Oct 21, 2019

Well done mix of theory and practice!

автор: Neil S

Sep 12, 2019

This is THE course to go with Sutton & Barto's Reinforcement Learning: An Introduction.

It's great to be able to repeat the examples from the book and end up writing code that outputs the same diagrams for e.g. Dyna-Q comparisons for planning. The notebooks strike a good balance between hand-holding for new topics and letting you make your own msitakes and learn from them.

I would rate five stars, but decided to drop one for now as there are still some glitches in the coding of Notebook assignments, requiring work-arounds communicated in the course forums. I hope these will be worked on and the course materials polished to perfection in future.

автор: Marius L

Sep 20, 2019

Overall, I found the course well made, inspiring and balanced. The videos really helped me to understand the rather austere textbook. I give 4 stars because some of the coding exercises felt more like work in progress, without the help of other students I would not have been able to overcome these issues.

автор: JDH

Sep 23, 2019

Rating 4.3 stars – so far (first two classes combined)

Lectures: 4.0stars

Quizes: 4.0stars

Programming assignments: 4.5stars

Book (Sutton and Barto): 4.5stars

In the spectrum from the theoretical to practical where you have, very roughly,...

(1) “Why”: Why you are doing what you are doing

(2) “What”: What you are doing

(3) “How”: How to implement it (eg programming)…

...this is a “what-how” class.

To cover the “why-what” I strongly recommend augmenting this class with David Silver’s lectures (on Youtube) and notes from a class he gave at UCL. This covers more of the theory/math behind RL but covers less on the coding. Combined together with this class it probably comprises the best RL education you can get *anywhere*, creating a 5-star combo.

http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

автор: Scott L

Sep 26, 2019

This course series is an incredible introduction to the basics of reinforcement learning, full stop. The course ... style, if you will, is a bit weird at first, but it seems to have been done on purpose with the aim of making the course somewhat timeless; they are presenting maths that will not change, in a format that will (hilariously) be no more slightly corny and weird in 2030 as it is in 2019.

автор: David C

Oct 10, 2019

A very good course. The lectures are brief and provide a quick overview of the topics. The quizzes require more in-depth reading to pass (covering material not discussed in the lectures) and the projects are difficult but rewarding and really help to cement the information. My only suggestion would be to lengthen the lectures to provide more discussion on the topics.

автор: Navid H

Oct 16, 2019

definitely interesting subjects, but I do not like the teaching method. Very mechanic and dull, with not enough connection to the real world