subject

Udacity: Reinforcement Learning

 with  Charles Isbell and Michael Littman
Sponsored
Project Management Certificate
Cornell University via eCornell
You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.

Why Take This Course?
This course will prepare you to participate in the reinforcement learning research community. You will also have the opportunity to learn from two of the foremost experts in this field of research, Profs. Charles Isbell and Michael Littman.

Syllabus

* Reinforcement Learning Basics
* Introduction to BURLAP
* TD Lambda
* Convergence of Value and Policy Iteration
* Reward Shaping
* Exploration
* Generalization
* Partially Observable MDPs
* Options
* Topics in Game Theory
* Further Topics in RL Models
6 Student
reviews
Cost Free Online Course
Pace Self Paced
Provider Udacity
Language English
Hours 6 hours a week
Calendar 16 weeks long

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6 reviews for Udacity's Reinforcement Learning

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a year ago
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Anonymous completed this course.
Extremely slow paced, with most of the topics handwaved. I've read Sutton's book. Topics (not all) from the book were covered on a shallow level. Additional topics were explained in such a way that you need to read other resources in order to actually implement them.

There are better books and resources on RL, don't waste time on this course
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2 out of 2 people found the following review useful
3 years ago
Vinayak Mehta audited this course, spending 10 hours a week on it and found the course difficulty to be hard.
The way in which the instructors teach is awesome.

This is a masters level machine learning course. I would recommend taking this course at a slow pace if you're a beginner in the machine learning domain, making sure that you get a thorough understanding of the material.
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10 months ago
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Anonymous completed this course.
Not as a first-time Reinforcement Learning course. You will have issues. Take David Silver's available on YouTube first and then come to this one. You'll enjoy it much more that way.
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a year ago
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Anonymous is taking this course right now.
Looks very slow and waste of time. Material is not matching with the lectures. Also, need to give codes for offline trials that only doing filups in lectures.
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2 out of 5 people found the following review useful
2 years ago
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Anonymous partially completed this course.
I am not assessing the whole course cause I couldn't make it through the first week because I didn't like it very much. The pace is very slow and the course doesn't seem to be very thorough. There is lots of redundant chit-chat, analogies and talking that doesn't contribute to better understanding. It's also quite cha Read More
I am not assessing the whole course cause I couldn't make it through the first week because I didn't like it very much. The pace is very slow and the course doesn't seem to be very thorough. There is lots of redundant chit-chat, analogies and talking that doesn't contribute to better understanding. It's also quite chaotic and unsystematic. Maybe it's a good idea for people that are not very bright and have no knowledge whatsoever on the topic, but otherwise I wouldn't recommend it. (As I mentioned my opinion is based solely on the first chapter - MDPs)
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11 months ago
Soonyau Cheong dropped this course, spending 2 hours a week on it and found the course difficulty to be medium.
The pace was extremely slow and there were too much nonsense going on between the two speakers. Just give the equations, explaination, examples and then move on!
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