Autonomous development of goals: From generic rewards to goal and self detection

Goals are abstractions that express agents´ intention and allow them to organize their behavior appropriately. How can agents develop such goals autonomously? This paper proposes a conceptual and computational account to this longstanding problem. We argue to consider goals as abstractions of lower-level intention mechanisms such as rewards and values, and point out that goals need to be considered alongside with a detection of the own actions´ effects. Then, both goals and self-detection can be learned from generic rewards. We show experimentally that task-unspecific rewards induced by visual saliency lead to self and goal representations that constitute goal-directed reaching.