Reinforcement Learning
link Source: www.udacity.com
list 16 sequences
assignment Level : Intermediate
chat_bubble_outline Language : English
card_giftcard 1 point
Users' reviews
-
starstarstarstarstar
0 reviews

Key Information

credit_card Free access

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.

more_horiz Read more
more_horiz Read less
dns

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
record_voice_over

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.
store

Content Designer

Georgia Institute of Technology

The Georgia Institute of Technology, also known as Georgia Tech or GT, is a co-educational public research university located in Atlanta, Georgia, USA. It is part of the wider University System of Georgia network. Georgia Tech has offices in Savannah (Georgia, USA), Metz (France), Athlone (Ireland), Shanghai (China), and Singapore.

Georgia Tech's reputation is built on its engineering and computer science programmes, which are among the best in the world5,6. The range of courses on offer is complemented by programmes in the sciences, architecture, humanities and management.

assistant

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.

You are the designer of this MOOC?
What is your opinion on this resource ?
Content
5/5
Platform
5/5
Animation
5/5