Chevron Left
返回到 Sample-based Learning Methods

学生对 阿尔伯塔大学 提供的 Sample-based Learning Methods 的评价和反馈

1,136 个评分


In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna...



Feb 14, 2021

Excellent course that naturally extends the first specialization course. The application examples in programming are very good and I loved how RL gets closer and closer to how a living being thinks.


Aug 11, 2020

Great course, giving it 5 stars though it deserves both because the assignments have some serious issues that shouldn't actually be a matter. All the other parts are amazing though. Good job


1 - Sample-based Learning Methods 的 25 个评论(共 221 个)

创建者 JD

Sep 22, 2019

创建者 Kaiwen Y

Oct 2, 2019

创建者 hope

Jan 25, 2020

创建者 Juan C E

Mar 7, 2020

创建者 Rishi R

Aug 3, 2020

创建者 Mukund C

Mar 17, 2020

创建者 Kinal M

Jan 10, 2020

创建者 Kyle A

Oct 3, 2019

创建者 Ivan S F

Sep 29, 2019

创建者 Manuel B

Nov 28, 2019

创建者 Amit J

Feb 27, 2021

创建者 Manuel V d S

Oct 4, 2019

创建者 Maxim V

Jan 12, 2020

创建者 Andrew G

Dec 24, 2019

创建者 Bernard C

Mar 22, 2020

创建者 Maximiliano B

Feb 23, 2020

创建者 Jonathan B

May 9, 2020

创建者 Steven W

May 11, 2021

创建者 Sandesh J

Jun 8, 2020

创建者 César S

Jul 9, 2021

创建者 Yover M C C

Apr 22, 2020

创建者 Alberto H

Oct 28, 2019

创建者 Karol P

Apr 9, 2021

创建者 Pars V

Jan 5, 2020

创建者 Surya K

Apr 12, 2020