Joy, Distress, Hope, and Fear in Reinforcement Learning (Extended Abstract)

In this paper we present a mapping between joy, distress, hope and fear, and Reinforcement Learning primitives. Joy / distress is a signal that is derived from the RL update signal, while hope/fear is derived from the utility of the current state. Agent-based simulation experiments replicate psychological and behavioral dynamics of emotion...