Robotics Institute
Seminar, June 14
Time
and Place | Seminar Abstract | Speaker
Biography | Speaker Appointments
Towards Real-Life Reinforcement Learning
Michael
L. Littman
Department of Computer
Science
Rutgers University
Piscataway, NJ USA
Mauldin Auditorium (NSH 1305)
Talk 10:30 am
The
reinforcement-learning hypothesis is that intelligent behavior arises from the
actions of an individual seeking to maximize its received reward signals in a
complex and changing world. This
perspective suggests a research program with the goal of understanding where
reward signals might come from and developing algorithms that search the space
of behaviors to maximize reward signals.
In the past15 years, great strides have been made in understanding
models and algorithms for reward optimization.
I will survey some of this work, and suggest what advances in
understanding will be needed to build successful learners in real-life
environments.
Michael Littman is director of the
Rutgers Laboratory for Real-Life Reinforcement Learning (RL^3) and his research
in machine learning examines algorithms for decision making under
uncertainty. After a memorable year at
CMU, Michael earned his Ph.D. from Brown
University in 1996, then worked as an
assistant professor at Duke University, a member of technical staff in AT&T's
AI Principles Research Department, and is now an associate professor of
computer science at Rutgers. He is on the executive council of the
American Association for AI, an advisory board member of the Journal of AI
Research and an action editor of the Journal of Machine Learning Research.
For appointments, please contact Geoffrey Gordon.
The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.
