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Using Cognitive Models to Train Warm Start Reinforcement Learning Agents for Human-Computer Interactions

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 نشر من قبل Chao Zhang
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
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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.



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