Title: Hierarchical Behavioral Repertoires with Unsupervised Descriptors,
Abstract: Enabling artificial agents to automatically learn complex, versatile and
high-performing behaviors is a long-lasting challenge. This paper presents a
step in this direction with hierarchical behavioral repertoires that stack
several behavioral repertoires to generate sophisticated behaviors. Each
repertoire of this architecture uses the lower repertoires to create complex
behaviors as sequences of simpler ones, while only the lowest repertoire
directly controls the agent's movements. This paper also introduces a novel
approach to automatically define behavioral descriptors thanks to an
unsupervised neural network that organizes the produced high-level behaviors.
The experiments show that the proposed architecture enables a robot to learn
how to draw digits in an unsupervised manner after having learned to draw lines
and arcs. Compared to traditional behavioral repertoires, the proposed
architecture reduces the dimensionality of the optimization problems by orders
of magnitude and provides behaviors with a twice better fitness. More
importantly, it enables the transfer of knowledge between robots: a
hierarchical repertoire evolved for a robotic arm to draw digits can be
transferred to a humanoid robot by simply changing the lowest layer of the
hierarchy. This enables the humanoid to draw digits although it has never been
trained for this task.