基于认知模型的训练用于人机交互的热启动强化学习Agent
Using Cognitive Models to Train Warm Start Reinforcement Learning Agents for Human-Computer Interactions
摘要：
人机交互应用中的强化学习（RL）代理需要重复的用户交互才能表现良好。为了解决这个“冷启动”问题，我们提出了一种新的方法，使用认知模型来预训练RL代理之前，他们被应用到真正的用户。在简要回顾了相关的认知模型，我们提出了我们的一般方法，其次是我们以前和正在进行的项目的两个案例研究。我们希望这份立场文件能激发RL，HCI和认知科学研究人员之间的对话，以探索该方法的全部潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
