Bob Price
Department of Computer Science
University of British Columbia
Vancouver, BC, V6T 1Z4
email: price@cs.ubc.ca
Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: cebly@cs.toronto.edu
Abstract
In multiagent environments, forms of social learning such as teaching
and imitation have been shown to aid the transfer of knowledge from
experts to learners in reinforcement learning (RL). We recast the
problem of imitation in a Bayesian framework. Our Bayesian
imitation model allows a learner to smoothly pool prior knowledge,
data obtained through interaction with the environment, and
information inferred from observations of expert agent behaviors. Our
model integrates well with recent Bayesian exploration techniques, and
can be readily generalized to new settings.
To appear, IJCAI-03
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