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学生对 University of Alberta 提供的 Sample-based Learning Methods 的评价和反馈

4.8
1,253 个评分

课程概述

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...

热门审阅

DP

Feb 14, 2021

Excellent course that naturally extends the first specialization course. The application examples in programming are very good and I loved how RL gets closer and closer to how a living being thinks.

DC

Aug 23, 2020

The material discussed is very clear, and the graded quizzes and programming assignments force you to really understand what you have just heard. I enjoyed this course a lot, and learned even more.

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51 - Sample-based Learning Methods 的 75 个评论(共 244 个)

创建者 AR

Mar 14, 2022

The videos are very clear and do a good job explaining the material from the textbook. The assignments are relevant and just right in terms of length and difficulty.

创建者 Gordon L W C

Feb 15, 2020

The course is intermediate in difficulty. But it explains the concept very clearly for me to understand difference between different sample based learning methods.

创建者 Raja S

Jun 19, 2022

Great course - well paced, with the right material. And the professors deliver content in a structured way, which makes it easier to understand complex concepts.

创建者 Art H

Apr 13, 2020

Well done. Follows Reinforcement Learning (Sutton/Barto) closely and explains topics well. Graded notebooks are invaluable in understanding the material well.

创建者 Kees J d V

Dec 19, 2020

Reinforcement Learning has added a whole new paradigm to my thinking. The course + book combination is perfect. The instructors are extremely good :D

创建者 Pablo S

Aug 2, 2023

Excellent material, excellent didactic, and the programming exercises provide the completion needed for the methods understanding, beautiful curse.

创建者 Karim D

Oct 20, 2020

Excellent course. Really well taught. Good pace of videos and assignments, with the support of perfect reading material. thank you tot he teachers.

创建者 Giulio C

Jul 13, 2020

Excellent course and instructors! I'm very excited about this specialization. They are able to explain hard concepts from the book in an easy way.

创建者 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.

创建者 Danish A

Jul 4, 2022

Excellent paced course that helped me understand sample based methods. Assignments were thoroughly build to practically utilize these concepts

创建者 Tianpei X

Aug 8, 2022

This coures is part of Reinforcement Learning specialization. You need to understand basic statistics to understand it. Good instruction !

创建者 DOMENICO P

Apr 19, 2020

One of most accurate, precise and well explained courses I have ever had with Coursera. Congratulations for teachers and course creators.

创建者 李谨杰

May 1, 2020

An excellent course!!!! This is the best course I have ever taken on Coursera! Thanks a lot to two supervisors and teaching assistants!

创建者 Leon Y

Jan 9, 2021

Awesome videos and homework! Great thanks to Prof. Martha White and Prof. Adam White! I do appreciate such educational opportunities!

创建者 S. K G P

Jun 11, 2020

I think it was one of the best courses to cover this topic. Clear and crisp presentations. Great programming assignments as well!!

创建者 Christian J R F

May 7, 2020

Excelent course, I would love to do some other exercises out of the grid world but in general the content is good and interesting.

创建者 Pokman C

Apr 8, 2021

Concepts and methods introduced in this course are well motivated and presented. The assignments are very thoughtfully designed.

创建者 Antonis S

May 9, 2020

Very well prepared and interesting course! I will seek more for sure in the future! Thank you so much for offering this course!

创建者 Batuhan S

Aug 26, 2023

It was very great course. Short and clearvideos eased to understand the topics,

coding assessments were also very informative.

创建者 La W N

Jul 28, 2020

I am really enjoying to learn reinforcement learning. The instructors are really good at explanation. Going for next course B)

创建者 Kiara O

Jan 7, 2020

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

创建者 John J

Apr 28, 2020

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

创建者 Piotr K

Feb 21, 2025

The course was very well taught by Adam and Martha. The coding assignments were well prepared with plenty of explanations.

创建者 nicole s

Feb 2, 2020

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

创建者 Nikhil G

Nov 25, 2019

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