Chevron Left
返回到 Prediction and Control with Function Approximation

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

731 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.


51 - Prediction and Control with Function Approximation 的 75 个评论(共 133 个)

创建者 Andrew G

Jan 26, 2020

创建者 Alexander P

Dec 14, 2019

创建者 Mathew

Jun 7, 2020

创建者 Ayan S

Jul 4, 2021

创建者 Johannes

Sep 13, 2021

创建者 Joosung M

Jun 14, 2020

创建者 Tolga K

Dec 25, 2020

创建者 J B

Oct 13, 2020

创建者 LI C Y

Aug 14, 2022

创建者 Eduardo I L H

Jan 14, 2021

创建者 Yitao H

Aug 29, 2021

创建者 Huang C

Jan 25, 2022


Nov 21, 2020

创建者 Artur M

Nov 3, 2020

创建者 George M

Mar 11, 2021

创建者 Chang, W C

Oct 14, 2019

创建者 Rishi R

Aug 3, 2020

创建者 Kaustubh S

Dec 24, 2019

创建者 Max C

Nov 1, 2019

创建者 Sergey M

Oct 15, 2021

创建者 Saulo A G S

Aug 12, 2022


Jan 27, 2020

创建者 Pachi C

Dec 31, 2019

创建者 석박통합김한준

Apr 25, 2020

创建者 Raktim P

Dec 17, 2019