Report Number: CS-TR-95-1552
Institution: Stanford University, Department of Computer Science
Title: Embedded Teaching of Reinforcement Learners
Author: Brafman, Ronen I.
Author: Tennenholtz, Moshe
Date: June 1995
Abstract: Knowledge plays an important role in an agent's ability to
perform well in its environment. Teaching can be used to
improve an agent's performance by enhancing its knowledge. We
propose a specific model of teaching, which we call embedded
teaching. An embedded teacher is an agent situated with a
less knowledgeable ``student'' in a common environment. The
teacher's goal is to lead the student to adopt a particular
desired behavior. The teacher's ability to teach is affected
by the dynamics of the common environment and may be limited
by a restricted repertoire of actions or uncertainty about
the outcome of actions; we explicitly represent these
limitations as part of our model. In this paper, we address a
number of theoretical issues including the characterization
of a challenging embedded teaching domain and the computation
of optimal teaching policies. We then incorporate these ideas
in a series of experiments designed to evaluate our ability
to teach two types of reinforcement learners.
http://i.stanford.edu/pub/cstr/reports/cs/tr/95/1552/CS-TR-95-1552.pdf