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学生对 阿尔伯塔大学 提供的 Prediction and Control with Function Approximation 的评价和反馈

726 个评分


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...



Apr 11, 2020

Difficult but excellent and impressing. Human being is incredible creating such ideas. This course shows a way to the state when all such ingenious ideas will be created by self learning algorithms.


Dec 1, 2019

Well peaced and thoughtfully explained course. Highly recommended for anyone willing to set solid grounding in Reinforcement Learning. Thank you Coursera and Univ. of Alberta for the masterclass.


76 - Prediction and Control with Function Approximation 的 100 个评论(共 133 个)

创建者 Ola D

Jun 15, 2022

F​antastic course with fantastic instructors

创建者 İbrahim Y

Oct 5, 2020

the course is the intro for high level RL

创建者 MJ A

Jan 23, 2021

perfect and thank you for this course

创建者 Teresa Y B

May 11, 2020

Very Useful and Highly Recommend !!!

创建者 Stewart A

Oct 31, 2019

Simply the best course on this topic.

创建者 Farzad E b

Aug 4, 2022

It was perfect, I really enjoyed it

创建者 Junchao

May 29, 2020

Very good and self-oriented course!

创建者 Fernando A S G

Mar 26, 2021

Excellent course! Thanks a lot!

创建者 Wei J

Oct 11, 2020

It is a very perfect RL course.

创建者 Antonis S

May 30, 2020

Really a well-prepared course!

创建者 Ignacio O

Nov 29, 2019

Really good, I learned a lot.


May 2, 2020

Great speakers and content!

创建者 Majd W

Feb 1, 2020

Very practical course.

创建者 李谨杰

Jun 17, 2020

Excellent class !!!

创建者 Mohamed A

Sep 11, 2021

v​ery good course

创建者 Hugo T K

Aug 18, 2020

Excellent course.

创建者 Murtaza K B

Apr 25, 2020

Excellent course

创建者 Ivan M

Aug 30, 2020

Just brilliant

创建者 Juan F L

Aug 3, 2022

great course!

创建者 Oriol A L

Nov 19, 2020

Very good!

创建者 Cheuk L Y

Jul 8, 2020

Very good!

创建者 Jialong F

Feb 23, 2021


创建者 Justin O

May 18, 2021



Feb 27, 2021


创建者 Ananthapadmanaban, J

Jul 19, 2020

I am disappointed with policy gradients being introduced on last week of the 3rd course. The instructors need to understand that 12 weeks is too much for introduction before starting a good project to implement the concepts with a hope to better understand them (course 4). Policy gradients should have been introduced in week 3/4 of course 2 itself. The content before that should be made more efficient (4 weeks to understand until q-learning/sarsa and 2 weeks to understand function approximation should be enough). I realized after course 2 that Andrew Ng has 3/4 videos on RL in the recently released ML class from Stanford. I am yet to go through them, but I feel they may explain these faster with same amount of rigour. However, the stanford class assignments are not public, which makes this course still useful because of the assignments. However, thanks to the instructors for this course.