Title:
Human-Focused Reinforcement Learning

dc.contributor.author Brunskill, Emma
dc.contributor.corporatename Georgia Institute of Technology. GVU Center en_US
dc.contributor.corporatename Stanford University. Dept. of Computer Science en_US
dc.date.accessioned 2020-03-09T18:08:32Z
dc.date.available 2020-03-09T18:08:32Z
dc.date.issued 2020-02-27
dc.description Presented on February 27, 2020 at 12:00 p.m. in the Technology Square Research Building, Banquet Hall. en_US
dc.description Emma Brunskill is an assistant professor in the Computer Science Department at Stanford University where she leads the AI for Human Impact group. Her work focuses on reinforcement learning in high stakes scenarios--how can an agent learn from experience to make good decisions when experience is costly or risky, such as in educational software, healthcare decision making, or people-facing applications. en_US
dc.description Runtime: 56:32 minutes en_US
dc.description.abstract There is increasing excitement about reinforcement learning--a subarea of machine learning for enabling an agent to learn to make good decisions. Yet numerous questions and challenges remain for reinforcement learning to help support progress in applications that involve interacting with people, like education, consumer marketing and healthcare. I will discuss our work on some of the technical challenges that arise in this pursuit, including sample efficiency, counterfactual reasoning, robustness, and applications to health and education. en_US
dc.format.extent 56:32 minutes
dc.identifier.uri http://hdl.handle.net/1853/62482
dc.language.iso en_US en_US
dc.relation.ispartofseries GVU Brown Bag
dc.subject Artificial intelligence (AI) en_US
dc.subject Human-focused AI en_US
dc.subject Reinforcement learning en_US
dc.title Human-Focused Reinforcement Learning en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename GVU Center
local.relation.ispartofseries GVU Brown Bag Seminars
relation.isOrgUnitOfPublication d5666874-cf8d-45f6-8017-3781c955500f
relation.isSeriesOfPublication 34739bfe-749f-4bc5-a716-21883cd1bbd0
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