Learning a set of interrelated tasks by using a succession of motor policies for a socially guided intrinsically motivated learner

We propose an active learning algorithmic architecture, capable of organizing its learning process in order to achieve a field of complex tasks by learning sequences of primitive motor policies : Socially Guided Intrinsic Motivation with Procedure Babbling (SGIM-PB). The learner can generalize over its experience to continuously learn new outcomes, by choosing actively what and how to learn guided by empirical measures of its own progress. In this paper, we are considering the learning of a set of interrelated complex outcomes hierarchically organized. We introduce a new framework called "procedures", which enables the autonomous discovery of how to combine previously learned skills in order to learn increasingly more complex motor policies (combinations of primitive motor policies). Our architecture can actively decide which outcome to focus on and which exploration strategy to apply. Those strategies could be autonomous exploration, or active social guidance, where it relies on the expertise of a human teacher providing demonstrations at the learner's request. We show on a simulated environment that our new architecture is capable of tackling the learning of complex motor policies, to adapt the complexity of its policies to the task at hand. We also show that our "procedures" increases the agent's capability to learn complex tasks.