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Reinforcement Learning

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.

Prerequisites and Requirements: Before taking this course, you should have taken a graduate-level machine-learning course and should have had some exposure to reinforcement learning from a previous course or seminar in computer science (students who have completed will be well prepared for this course).Additionally, you will be programming extensively in Java during this course. If you are not familiar with Java, we recommend you review Udacity's course materials to get up to speed beforehand.See the for using Udacity.

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Rating Not enough ratings
Length Approx. 4 months
Starts On Demand (Start anytime)
Cost $0
From Udacity
Instructors Charles Isbell, Michael Littman, Chris Pryby
Download Videos Only via web browser
Language English
Subjects Programming
Tags Computer Science Software Development

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Udacity

Rating Not enough ratings
Length Approx. 4 months
Starts On Demand (Start anytime)
Cost $0
From Udacity
Instructors Charles Isbell, Michael Littman, Chris Pryby
Download Videos Only via web browser
Language English
Subjects Programming
Tags Computer Science Software Development

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