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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

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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!

筛选依据：

76 - Prediction and Control with Function Approximation 的 100 个评论（共 148 个）

创建者 Artur M

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Nov 3, 2020

Great course! Wished to see more about policy gradient methods, but it was awesome.

创建者 George M

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Mar 11, 2021

Comprehensive and intensive course.

More challenging than the previous two courses.

创建者 WC C

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Oct 14, 2019

The course presentation is wonderful. I can't stop after I watch the first video.

创建者 Rishi R

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Aug 3, 2020

It has amazing content with no compromise on concepts yet holds simplicity.

创建者 Ali N

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Mar 26, 2024

so good and useful! the value of this course is more of all previouses!

创建者 Kaustubh S

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Dec 24, 2019

It was a wonderful course. To the point yet well-explained concepts.

创建者 Max C

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Nov 1, 2019

I had a much better experience with the autograder than in course 2.

创建者 Sergey M

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Oct 15, 2021

Very nice and helpful course, very well organized and explained.

创建者 helia

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Jun 9, 2023

This course was one of the best courses I have ever taken :)

创建者 Saulo A G S

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Aug 12, 2022

The contents are so important for applications based on AI

创建者 Seyed K M Z

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Jun 5, 2023

Well taught! I recommend to anyone who seeks to learn RL.

创建者 LIWANGZHI

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Jan 26, 2020

Everything is amazing in this course! Dont miss it!

创建者 Pachi C

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Dec 31, 2019

Fantastic course and great content and teachers!!!

创建者 김한준

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Apr 25, 2020

Excellent course! Never be replaced! Thank you!

创建者 Raktim P

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Dec 17, 2019

Great Course! Highly recommended for beginners.

创建者 Ola D

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Jun 15, 2022

Fantastic course with fantastic instructors

创建者 İbrahim Y

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Oct 5, 2020

the course is the intro for high level RL

创建者 MJ A

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Jan 23, 2021

perfect and thank you for this course

创建者 Teresa Y B

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May 11, 2020

Very Useful and Highly Recommend !!!

创建者 Stewart A

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Oct 31, 2019

Simply the best course on this topic.

创建者 Farzad E b

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Aug 4, 2022

It was perfect, I really enjoyed it

创建者 Junchao

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May 29, 2020

Very good and self-oriented course!

创建者 Fernando A S G

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Mar 26, 2021

Excellent course! Thanks a lot!

创建者 Wei J

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Oct 11, 2020

It is a very perfect RL course.

创建者 Antonis S

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May 30, 2020

Really a well-prepared course!