Reinforcement Learning
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assignment Level : Introductive
chat_bubble_outline Language : English
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Key Information

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About the content

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.

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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
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Instructors

  • Charles Isbell - Charles Isbell is a Professor and Senior Associate Dean at the School of Interactive Computing at Georgia Tech. His research passion is artificial intelligence, particularly on building autonomous agents that must live and interact with large numbers of other intelligent agents, some of whom may be human. Lately, he has turned his energies toward adaptive modeling, especially activity discovery (as distinct from activity recognition), scalable coordination, and development environments that support the rapid prototyping of adaptive agents. He is developing adaptive programming languages, and trying to understand what it means to bring machine learning tools to non-expert authors, designers and developers. He sometimes interacts with the physical world through racquetball, weight-lifting and Ultimate Frisbee.
  • Michael Littman - Michael Littman is a Professor of Computer Science at Brown University. He also teaches Udacity’s Algorithms course (CS215) on crunching social networks. Prior to joining Brown in 2012, he led the Rutgers Laboratory for Real-Life Reinforcement Learning (RL3) at Rutgers, where he served as the Computer Science Department Chair from 2009-2012. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), served as program chair for AAAI's 2013 conference and the International Conference on Machine Learning in 2009, and received university-level teaching awards at both Duke and Rutgers. Charles Isbell taught him about racquetball, weight-lifting and Ultimate Frisbee, but he's not that great at any of them. He's pretty good at singing and juggling, though.
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Content Designer

Georgia Institute of Technology
The Georgia Institute of Technology is one of the nation's top research universities, distinguished by its commitment to improving the human condition through advanced science and technology. Georgia Tech's campus occupies 400 acres in the heart of the city of Atlanta, where more than 20,000 undergraduate and graduate students receive a focused, technologically based education.
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Platform

Udacity

Udacity is a for-profit educational organization founded by Sebastian Thrun, David Stavens, and Mike Sokolsky offering massive open online courses (MOOCs). According to Thrun, the origin of the name Udacity comes from the company's desire to be "audacious for you, the student". While it originally focused on offering university-style courses, it now focuses more on vocational courses for professionals.

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