Generalizing Inverse Reinforcement Learning for the Real World
Associate Professor of Computer Science
University of Georgia
Tuesday, April 21, 2015|
4:00pm - 5:30pm
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About the Event
Inverse reinforcement learning (IRL) investigates ways by which a learner may approximate the preferences of an expert by observing the experts’ actions over time. Usually, the expert is modeled as optimizing its actions using a Markov decision process (MDP), whose parameters except for the reward function are known to the learner. IRL is enjoying much success and attention in applications such as robots learning tasks from demonstrations by human experts and in imitation learning.
Dr. Prashant Doshi is an Associate Professor of Computer Science, director of the THINC Lab (http://thinc.cs.uga.edu) and is a faculty member of the AI Institute at the University of Georgia, USA. He is also serving as the founding director of the Faculty of Robotics at UGA (http://robotics.uga.edu) -- an OVPR initiative. He received his Ph.D. in Computer Science from the University of Illinois at Chicago in 2005. His research interests lie in decision making under uncertainty in multiagent settings, robotics and the semantic Web. His research has been funded by grants from NSF (including the CAREER in 2008), AFOSR, ARO and ONR. He received UGA's Creative Research Medal in 2011 for his contributions to automated decision making. He has published extensively in JAIR, conferences such as AAAI and AAMAS, and other forums in the fields of agents and AI. Currently, he is on sabbatical with the Cheriton School of Computer Science at the University of Waterloo.
Open to: Public