  This paper studies the coupling of internally guided learning and social
interaction, and more specifically the improvement owing to demonstrations of
the learning by intrinsic motivation. We present Socially Guided Intrinsic
Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous,
unbounded and non-preset environments. After introducing social learning and
intrinsic motivation, we describe the design of our algorithm, before showing
through a fishing experiment that SGIM-D efficiently combines the advantages of
social learning and intrinsic motivation to gain a wide repertoire while being
specialised in specific subspaces.
