When Exploration is Expensive -- Reducing and Bounding the Amount of Experience Needed to Learn to Make Good Decisions [ISL]

Topic: 
When Exploration is Expensive -- Reducing and Bounding the Amount of Experience Needed to Learn to Make Good Decisions
Thursday, April 27, 2017 - 4:15pm
Venue: 
Packard 101
Speaker: 
Emma Brunskill (Stanford)
Abstract / Description: 

Understanding the limits of how much experience is needed to learn to make good decisions is both a foundational issue in reinforcement learning, and has important applications. Indeed, the potential to have artificial agents that help augment human capabilities, in the form of automated coaches or teachers, is enormous. Such reinforcement learning agents must explore in costly domains, since each experience comes from interacting with a human. I will discuss some of our recent theoretical results on sample efficient reinforcement learning.


 

The Information Systems Laboratory Colloquium (ISLC) is typically held in Packard 101 every Thursday at 4:15 pm during the academic year. Refreshments are usually served after the talk.

The Colloquium is organized by graduate students Martin Zhang, Farzan Farnia, Reza Takapoui, and Zhengyuan Zhou. To suggest speakers, please contact any of the students.