Joy, Distress, Hope, and Fear in Reinforcement Learning

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 including: joy and distress reactions that develop prior to hope and fear; fear extinction; habituation of joy; and, task randomness that increases the intensity of joy and distress. This work distinguishes itself by assessing the dynamics of emotion in an adaptive agent framework-coupling it to the literature on habituation, development, and extinction.