Prediction and Control with Function Approximation に戻る

4.8

78件の評価

•

11件のレビュー

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...

Nov 05, 2019

Great Learning, the best part was the Actor-Critic algorithm for a small pendulum swing task all from stratch using RLGLue library. Love to learn how experimentation in RL works.

Nov 10, 2019

Great course. Slightly more complex than courses 1 and 2, but a huge improvement in terms of applicability to real-world situations.

フィルター：

by Mateusz K

•Oct 29, 2019

Its got a great variety of very applicable examples, use cases, and assignments. May be tough if people don't quite understand how neural networks work, so I suggest having a basic understanding of NN for parts of this course.

by Stewart A

•Oct 31, 2019

Simply the best course on this topic.

by Max C

•Nov 01, 2019

I had a much better experience with the autograder than in course 2.

by Akash B

•Nov 05, 2019

Great Learning, the best part was the Actor-Critic algorithm for a small pendulum swing task all from stratch using RLGLue library. Love to learn how experimentation in RL works.

by Chang, W C

•Oct 15, 2019

The course presentation is wonderful. I can't stop after I watch the first video.

by Mark J

•Oct 23, 2019

This, the third in an exceptionally well-paced series of four courses on Reinforcement Learning, extends the scope of the subject to include parameterized functions (i.e., neural networks). The section on tiling methods is especially interesting. The course is taught under the auspices of professors who, quite literally, wrote the book on reinforcement learning, and includes several video lectures by leading practitioners and theorists in the field. The final programming assignment, in particular, made me feel like I did when I wrote my first computer program that actually did what it was supposed to way back when -- delight and amazement.

by Ivan S F

•Nov 10, 2019

Great course. Slightly more complex than courses 1 and 2, but a huge improvement in terms of applicability to real-world situations.

by Julien T

•Nov 12, 2019

Great course and specialization. The teachers are great, the material well presented and balanced. I strongly recommend this course to anyone interested in the field of Reinforcement Learning. For maximum chance of success I suggest following all 3 courses in succession and investing the necessary amount of time to read the textbook chapters as specified at the beginning of each week.

Looking forward to completing the capstone project now!

by Navid H

•Oct 16, 2019

The material is very good. But this course needs better instructors/ method of teaching. The book is also written in an unnecessarily technical way filled with jargon. explanations are not clear, simple stuff is presented in a very complicated manner for no obvious way.

by RJT

•Oct 17, 2019

Course is great! Maybe some slides would be helpful not to forget.

by Luiz C

•Oct 03, 2019

Almost perfect, except two ~minor objections:

1/ the learning content between the 4 weeks is quite unbalanced. The initial weeks of the course are well sized, whereas week #3 and week #4 feel a touch light. It feels like the Instructors rushed to make the Course available online, and didn't have time to put as much content as they wished in the last weeks of the Course

2/ there are too many typos in some notebooks (specifically notebook of week #3). It gives the impression it was made in a rush, and nobody read over it again. Besides there seems to currently be some issue with this assignment