“Reinforcement
Learning: From Vision to Action and Back”
Stemming in part
from the great successes of other areas of Machine Learning, in particular from
the recent success of Deep Learning, there is renewed hope and interest in
Reinforcement Learning (RL) from the wider applications communities. Indeed,
there is a recent burst of new and exciting progress in both theory and
practice of RL. I will describe some results from my own group on a simple new
connection between planning horizon and overfitting in RL, as well as some
results on combining RL with Deep Learning in Atari games and Minecraft. I will
conclude with some lookahead at what we can do, both as theoreticians and those
that collect data, to accelerate the impact of RL.
Speaker:
Satinder Singh is a
Professor of Computer Science and Engineering as well as the Director of the
Artificial Intelligence Laboratory at the University of Michigan, Ann Arbor. He
has been the Chief Scientist at Syntek Capital, a venture capital company, a
Principal Research Scientist at AT&T Labs, an Assistant Professor of
Computer Science at the University of Colorado, Boulder, and a Postdoctoral
Fellow at MIT’s Brain and Cognitive Science department. His research focus is
on developing the theory, algorithms and practice of building artificial agents
that can learn from interaction in complex, dynamic, and uncertain
environments, including environments with other agents in them. His main
contributions have been to the areas of reinforcement learning, multi-agent
learning, and more recently to applications in cognitive science and
healthcare. He is a Fellow of the AAAI (Association for the Advancement of
Artificial Intelligence) and has coauthored more than 150 refereed papers in
journals and conferences.
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