Chevron Left
Prediction and Control with Function Approximation に戻る

アルバータ大学(University of Alberta) による Prediction and Control with Function Approximation の受講者のレビューおよびフィードバック

4.8
749件の評価

コースについて

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...

人気のレビュー

WP

2020年4月11日

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.

AC

2019年12月1日

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.

フィルター:

Prediction and Control with Function Approximation: 101 - 125 / 133 レビュー

by PHILIP C

2021年6月18日

by Luiz C

2019年10月3日

by Luka K

2021年1月4日

by Dmitry S

2020年1月5日

by Hadrien H

2021年2月4日

by Steven W

2021年5月11日

by Narendra G

2020年7月19日

by Nicolas M

2020年10月24日

by Lucas O S

2020年1月21日

by Anirban D

2022年7月24日

by 남상혁

2021年1月17日

by Hugo V

2020年1月15日

by Amit J

2021年3月17日

by Lik M C

2020年1月18日

by Mark P

2020年8月17日

by Anton P

2020年4月12日

by Vladyslav Y

2020年9月8日

by Sharang P

2020年2月27日

by Jerome b

2020年4月9日

by Rajesh M

2020年4月17日

by SCOTT A

2020年8月5日

by Muhammed A Ç

2021年9月4日

by Pouya E

2020年12月2日

by Rishabh K

2020年5月19日

by Ramaz J

2019年10月17日