Using informative behavior to increase engagement while learning from human reward

In this work, we address a relatively unexplored aspect of designing agents that learn from human reward. We investigate how an agent’s non-task behavior can affect a human trainer’s training and agent learning. We use the TAMER framework, which facilitates the training of agents by human-generated...

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Bibliographic Details
Main Authors: Li, Guangliang, Whiteson, Shimon, Knox, W. Bradley, Hung, Hayley
Other Authors: Massachusetts Institute of Technology. Personal Robots Group
Format: Article
Language:English
Published: Springer US 2016
Online Access:http://hdl.handle.net/1721.1/103607