Joy in Learning

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 prio...

The Royal College and the dying joy of learning.

A reinforcement learning model of joy, distress, hope and fear

In this paper we computationally study the relation between adaptive behavior and emotion. Using the Reinforcement Learning framework, we propose that learned state utility, V(s), models fear (negative) and hope (positive) based on the fact that both signals are about anticipation of loss or gain. Further, we propose that joy/distress is a signal similar to the error signal. We present agent-ba...

Joy Mining Machinery

Joy is in a unique position to provide comments on this subject as we have been involved with Proximity Detection since the initial research project conducted in 2002 as documented in the Background information of this Information Request. In addition we have been involved to some level with each Proximity Detection manufacturer on every generation of design because of the interface to our cont...

The biology of joy.

Richard Davidson was in a lab observing a Buddhist Monk Sink deep into serene meditation when he noticed something that sent his own pulse racing. Davidson, a professor of psychology and psychiatry at the University of Wisconsin, hurriedly double-checked the data streaming to his computer from electrodes attached to the monk's skull, but there was no mistake. Electrical activity in the left pre...