  Although humans live in an open-ended world and endlessly face new
challenges, they do not have to learn from scratch each time they face the next
one. Rather, they have access to a handful of previously learned skills, which
they rapidly adapt to new situations. In artificial intelligence, autotelic
agents, which are intrinsically motivated to represent and set their own goals,
exhibit promising skill adaptation capabilities. However, these capabilities
are highly constrained by their policy and goal space representations. In this
paper, we propose to investigate the impact of these representations on the
learning and transfer capabilities of autotelic agents. We study different
implementations of autotelic agents using four types of Graph Neural Networks
policy representations and two types of goal spaces, either geometric or
predicate-based. By testing agents on unseen goals, we show that combining
object-centered architectures that are expressive enough with semantic
relational goals helps learning to reach more difficult goals. We also release
our graph-based implementations to encourage further research in this
direction.
