  Autonomy is fundamental for artificial agents acting in complex real-world
scenarios. The acquisition of many different skills is pivotal to foster
versatile autonomous behaviour and thus a main objective for robotics and
machine learning. Intrinsic motivations have proven to properly generate a
task-agnostic signal to drive the autonomous acquisition of multiple policies
in settings requiring the learning of multiple tasks. However, in real world
scenarios tasks may be interdependent so that some of them may constitute the
precondition for learning other ones. Despite different strategies have been
used to tackle the acquisition of interdependent/hierarchical tasks, fully
autonomous open-ended learning in these scenarios is still an open question.
Building on previous research within the framework of intrinsically-motivated
open-ended learning, we propose an architecture for robot control that tackles
this problem from the point of view of decision making, i.e. treating the
selection of tasks as a Markov Decision Process where the system selects the
policies to be trained in order to maximise its competence over all the tasks.
The system is then tested with a humanoid robot solving interdependent multiple
reaching tasks.
