  Reinforcement learning (RL) agents in human-computer interactions
applications require repeated user interactions before they can perform well.
To address this "cold start" problem, we propose a novel approach of using
cognitive models to pre-train RL agents before they are applied to real users.
After briefly reviewing relevant cognitive models, we present our general
methodological approach, followed by two case studies from our previous and
ongoing projects. We hope this position paper stimulates conversations between
RL, HCI, and cognitive science researchers in order to explore the full
potential of the approach.
