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學生對 阿尔伯塔大学 提供的 Sample-based Learning Methods 的評價和反饋

4.8
790 個評分
159 條評論

課程概述

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...

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AA
2020年8月11日

Great course, giving it 5 stars though it deserves both because the assignments have some serious issues that shouldn't actually be a matter. All the other parts are amazing though. Good job

KM
2020年1月9日

Really great resource to follow along the RL Book. IMP Suggestion: Do not skip the reading assignments, they are really helpful and following the videos and assignments becomes easy.

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51 - Sample-based Learning Methods 的 75 個評論(共 155 個)

創建者 Kiara O

2020年1月7日

This course is well explained, easy to follow and made me understand much better the tabular RL methods. I liked it very much.

創建者 John J

2020年4月28日

This second instalment in the reinforcement learning journey is amazing. Although you can get stuck sometimes in some places.

創建者 nicole s

2020年2月2日

I like the teaching style the emphasis on understanding and the fruitful combination with the textbook. Highly recommended!

創建者 Nikhil G

2019年11月25日

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

創建者 Lik M C

2020年1月10日

Again, the course is excellent. The assignments are even better than Course 1. A really great course worth to take!

創建者 Zhang d

2020年4月7日

It is a wonderful and meanningful course, which can teach us the knowledge of Q-learning, expected Sarsa and so on.

創建者 Xingbei W

2020年3月8日

Although I have learned q learning and td, this course still give me a lot of new feeling and understanding on it.

創建者 Mathew

2020年6月7日

Very well structured and a great compliment to the Reinforcement Learning (2nd Edition) book by Sutton and Barto.

創建者 Stewart A

2019年9月3日

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

創建者 Martin P

2020年5月30日

A very interesting topic presented in an easy to consume form. It was fun learning with this course.

創建者 석박통합김한준

2020年4月7日

The course is spectacular! I've learned countless material on Reinforcement learning! Thank you!

創建者 Roberto M

2020年3月28日

The course is well organized and teachers provide a lot of examples to facilitate comprehension.

創建者 Chintan K

2020年7月22日

the course videos were short and precise , which makes the learning content fun and informative

創建者 Wang G

2019年10月19日

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

創建者 Sodagreenmario

2019年9月18日

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

創建者 Chris D

2020年4月18日

Very good. Minor issues with inconsistency between parameter naming in different exercises.

創建者 Sirusala N S

2020年7月30日

The concepts were explained very clearly. The assignments were helpful in understanding.

創建者 koji t

2019年10月6日

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

It ’s a wonderful course.

創建者 Louis S

2020年6月5日

Excellent content. The fact that it follows Sutton and Barto's TextBook is a must.

創建者 Ding L

2020年4月24日

By taking the class, I learned much more than only reading the textbook.

創建者 Ofir E

2020年3月22日

Amazing course, truly academy-grade. And RL is such a fascinating topic!

創建者 Fabrice L

2020年11月14日

Things start to get interesting in this course of the specialization.

創建者 Sourav G

2020年3月10日

It was a very good course. All the concepts were explained very well.

創建者 Animesh

2020年5月28日

this course is very well designed and executed. wow! i loved it :D

創建者 Li W

2020年3月30日

Very good introductions and practices to the classic RL algorithms