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Voltar para Prediction and Control with Function Approximation

Comentários e feedback de alunos de Prediction and Control with Function Approximation da instituição Universidade de AlbertaUniversidade de Alberta

753 classificações

Sobre o curso

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...

Melhores avaliações


1 de dez de 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.


24 de jun de 2020

Surely a level-up from the previous courses. This course adds to and extends what has been learned in courses 1 & 2 to a greater sphere of real-world problems. Great job Prof. Adam and Martha!

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76 — 100 de 133 Avaliações para o Prediction and Control with Function Approximation

por Ola D

15 de jun de 2022

Fantastic course with fantastic instructors

por İbrahim Y

5 de out de 2020

the course is the intro for high level RL

por MJ A

23 de jan de 2021

perfect and thank you for this course

por Teresa Y B

11 de mai de 2020

Very Useful and Highly Recommend !!!

por Stewart A

31 de out de 2019

Simply the best course on this topic.

por Farzad E b

4 de ago de 2022

It was perfect, I really enjoyed it

por Junchao

29 de mai de 2020

Very good and self-oriented course!

por Fernando A S G

26 de mar de 2021

Excellent course! Thanks a lot!

por Wei J

11 de out de 2020

It is a very perfect RL course.

por Antonis S

30 de mai de 2020

Really a well-prepared course!

por Ignacio O

29 de nov de 2019

Really good, I learned a lot.


2 de mai de 2020

Great speakers and content!

por Majd W

1 de fev de 2020

Very practical course.

por 李谨杰

17 de jun de 2020

Excellent class !!!

por Mohamed A

11 de set de 2021

very good course

por Hugo T K

18 de ago de 2020

Excellent course.

por Murtaza K B

25 de abr de 2020

Excellent course

por Ivan M

30 de ago de 2020

Just brilliant

por Juan “ L

3 de ago de 2022

great course!

por Oriol A L

19 de nov de 2020

Very good!

por Cheuk L Y

8 de jul de 2020

Very good!

por Jialong F

23 de fev de 2021


por Justin O

18 de mai de 2021


por Artod

27 de fev de 2021


por Ananthapadmanaban, J

19 de jul de 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.