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学生对 University of Alberta 提供的 Prediction and Control with Function Approximation 的评价和反馈

4.8
842 个评分

课程概述

In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment...

热门审阅

IF

Nov 9, 2019

Great course. Slightly more complex than courses 1 and 2, but a huge improvement in terms of applicability to real-world situations.

DL

May 31, 2020

I had been reading the book of Reinforcement Learning An Introduction by myself. This class helped me to finish the study with a great learning environment. Thank you, Martha and Adam!

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51 - Prediction and Control with Function Approximation 的 75 个评论(共 148 个)

创建者 Doug

May 21, 2021

This specialization is a gift to humanity. It should have been inscribed into the golden disc of the Voyager and shared with the aliens.

创建者 Casey S S

Feb 11, 2021

this course bridged the gap to Deep Learning, the most exciting direction in RL. I would like a sequel dedicated to this from U Alberta

创建者 Bhooshan V

Sep 3, 2021

Really enjoyed every part of the course. Programming assignments are helpful in asserting the theoretical understanding of the subject.

创建者 Kinal M

Jan 12, 2020

A great and interactive course to learn about using function approximation for control. Great way to learn DRL and its alternatives.

创建者 Ivan S F

Nov 9, 2019

Great course. Slightly more complex than courses 1 and 2, but a huge improvement in terms of applicability to real-world situations.

创建者 Yingping Z

Jan 2, 2021

Very nice a biref introduction to sutton's book! But seems to leave out somt charpter in the book which makes me a little unhappy.

创建者 Pablo S

Aug 11, 2023

Really Fantastic, the previous courses materials get into a more practical formulation to problems closer to real world situations

创建者 Jicheng F

Jul 11, 2020

Martha and Adam are excellent instructors. This course is so well organized and presented. I have learned a lot! Thanks very much!

创建者 Francois R

Sep 11, 2023

Great content, Great presentation.

I really appreciate the efforts that were made to create this comprehensive course.

Thank you.

创建者 Tri W G

Mar 27, 2020

Give nive theoretical foundation. I found RL courses are abstract, but the programming assignment give a nice conceptualization.

创建者 Andrew G

Jan 26, 2020

Did a good job of attaching a programming assignment to each lesson and giving clear and detailed instructions throughout

创建者 Alexander P

Dec 14, 2019

Great course on more advanced reinforcement learning techniques. Can't wait to apply these new skills in the wild.

创建者 Mathew

Jun 7, 2020

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

创建者 Ayan S

Jul 4, 2021

I really liked the lectures and how they clearly explained all the necessary details of such difficult topic.

创建者 Hannes

Sep 12, 2021

This course is as excellent as its predecessors! Well-structured, engaging and with clear explanations.

创建者 Joosung M

Jun 14, 2020

The course materials were very informative, the assignments were challenging enough. Highly recommended!

创建者 Tolga K

Dec 25, 2020

Great course, great material and notebooks like previous courses. It was a great experience. Thank you!

创建者 J B

Oct 13, 2020

Very helpful course. Excellent delivery and practical labs. There's even someone helping in the forum!

创建者 Shubh A

Jul 21, 2023

Most challenging part of the entire series till now, hoping to implement all of these in real world

创建者 LI C Y

Aug 14, 2022

Without these video lectures, it is not easy to understand some difficult contents in the textbooks.

创建者 Eduardo I L H

Jan 14, 2021

Excellent course. Focused in the theory of function approximation for reinforcement learning.

创建者 Yitao H

Aug 28, 2021

Intellectually challenging experience to combine supervised learning into RL framework!

创建者 Lieven D C

Jul 9, 2025

This is well built course with a solid academic foundation and a practical application.

创建者 Huang C

Jan 24, 2022

Great course to take for combining function approximations with reinforcement learning

创建者 RICARDO A F S

Nov 21, 2020

A great course, I took a long time doing the assignments, but in the end I solved it