BIB-VERSION:: CS-TR-v2.0
          ID:: STAN//CS-TR-95-1552
       ENTRY:: July 17, 1995
ORGANIZATION:: Stanford University, Department of Computer Science
       TITLE:: Embedded Teaching of Reinforcement Learners
        TYPE:: Technical Report
      AUTHOR:: Brafman, Ronen I.
      AUTHOR:: Tennenholtz, Moshe
        DATE:: June 1995
       PAGES:: 16
    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.
       NOTES:: [Adminitrivia V1/Prg/19950717]
         END:: STAN//CS-TR-95-1552