• Reinforcement learning's option switches are analogous to psychological insight. • Insight and options reveal comparable capabilities for transformational creativity. • Open problems remain: lifelong learning, switching when exploring, option discovery.  a r t i c l e i n f o Although creativity is studied from philosophy to cognitive robotics, a definition has proven elusive. We argue for emphasizing the creative process (the cognition of the creative agent), rather than the creative product (the artifact or behavior). Owing to developments in experimental psychology, the process approach has become an increasingly attractive way of characterizing creative problem solving. In particular, the phenomenon of insight, in which an individual arrives at a solution through a sudden change in perspective, is a crucial component of the process of creativity. These developments resonate with advances in machine learning, in particular hierarchical and modular approaches, as the field of artificial intelligence aims for general solutions to problems that typically rely on creativity in humans or other animals. We draw a parallel between the properties of insight according to psychology and the properties of Hierarchical Reinforcement Learning (HRL) systems for embodied agents. Using the Creative Systems Framework developed by Wiggins and Ritchie, we analyze both insight and HRL, establishing that they are creative in similar ways. We highlight the key challenges to be met in order to call an artificial system ''insightful''.
On a more abstract level, Colin et al. REF investigated creativity for problem-solving in artificial agents in the context of hierarchical reinforcement learning by emphasising parallels to psychology.
Hierarchical reinforcement learning as creative problem solving
Robotics Auton. Syst.
Robotics Auton. Syst.
Computer Science