Chevron Left
Вернуться к Sample-based Learning Methods

Отзывы учащихся о курсе Sample-based Learning Methods от партнера Альбертский университет

4.8
звезд
Оценки: 1,136

О курсе

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...

Лучшие рецензии

DP

14 февр. 2021 г.

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.

AA

11 авг. 2020 г.

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

Фильтр по:

1–25 из 221 отзывов о курсе Sample-based Learning Methods

автор: JD

22 сент. 2019 г.

автор: Kaiwen Y

2 окт. 2019 г.

автор: hope

25 янв. 2020 г.

автор: Juan C E

7 мар. 2020 г.

автор: Rishi R

3 авг. 2020 г.

автор: Mukund C

17 мар. 2020 г.

автор: Kinal M

10 янв. 2020 г.

автор: Kyle A

3 окт. 2019 г.

автор: Ivan S F

29 сент. 2019 г.

автор: Manuel B

28 нояб. 2019 г.

автор: Amit J

27 февр. 2021 г.

автор: Manuel V d S

4 окт. 2019 г.

автор: Maxim V

12 янв. 2020 г.

автор: Andrew G

24 дек. 2019 г.

автор: Bernard C

22 мар. 2020 г.

автор: Maximiliano B

23 февр. 2020 г.

автор: Jonathan B

9 мая 2020 г.

автор: Steven W

11 мая 2021 г.

автор: Sandesh J

8 июня 2020 г.

автор: César S

9 июля 2021 г.

автор: Yover M C C

22 апр. 2020 г.

автор: Alberto H

28 окт. 2019 г.

автор: Karol P

9 апр. 2021 г.

автор: Pars V

5 янв. 2020 г.

автор: Surya K

12 апр. 2020 г.