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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

4.7
927 個評分
192 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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101 - Sample-based Learning Methods 的 125 個評論(共 188 個)

創建者 Ignacio O

2019年10月13日

Great, informative and very interesting course.

創建者 Ashish S

2019年9月16日

A good course with proper Mathematical insights

創建者 Cheuk L Y

2020年7月3日

Very good overall! It takes time to digest.

創建者 LIWANGZHI

2020年1月15日

A nice course with well-designed homework:)

創建者 Jingxin X

2020年5月26日

Very helpful follow up tot he first one.

創建者 Ryan

2021年1月17日

Better than reading the textbook alone.

創建者 Sriram R

2019年10月20日

Well done mix of theory and practice!

創建者 Luiz C

2019年9月13日

Great Course. Every aspect top notch

創建者 Alejandro D

2019年9月19日

Excellent content and delivery.

創建者 Bekay K

2020年7月4日

Great resource to learning RL

創建者 PRIYA S

2020年6月1日

Great Course by great faculty!

創建者 Daniel W

2020年7月18日

Hard but a really good course

創建者 Pachi C

2019年12月8日

Great and fantastic course!!!

創建者 rashid K

2019年11月12日

Best RL course ever done

創建者 MD M R S

2021年3月4日

Awesome!!!!!!!!!!!!!

創建者 Eleni F

2020年3月15日

i really enjoy it!

創建者 Guoxiang Z

2021年3月7日

Very nice course!

創建者 ABHILASH N

2020年8月7日

Brilliant Course!

創建者 Julio E F

2020年6月29日

Amazing course!

創建者 Santiago M C

2020年5月20日

excelent course

創建者 Tran Q M

2020年2月17日

wondrous course

創建者 Jordan L

2020年9月29日

great lecture

創建者 Ricardo A F S

2020年9月5日

Great course

創建者 Antonio P

2019年12月13日

Great Course