Engineering and Reverse Engineering Reinforcement Learning

Engineering and Reverse Engineering Reinforcement Learning

Engineering and Reverse Engineering Reinforcement LearningJuly 18, 2014

Summary: Psychologists and neuroscientists routinely borrow ideas from machine learning to understand and model reinforcement learning in humans and animals. Likewise, ideas from psychology and neuroscience filter into machine learning in a variety of ways. The goal of the workshop is to highlight some of the theoretical synergies that have arisen from this cross-pollination. The symposium will cover three topics (see below), each addressed by one cognitive scientist/neuroscientist and one computer scientist.

Speakers

Learning to learn
Michael Littman (Brown)
Michael Frank (Brown)

Inverse reinforcement learning and theory of mind
Monica Babes-Vroman (Rutgers)
Chris Baker (MIT)

Intrinsic motivation and exploration
Laura Schulz (MIT)
Andrew Barto (UMass Amherst)

Workshop is co-organized by the MIT Intelligence Initiative (MIT I^2) and the Center for Brains, Minds and Machines (CBMM.)