Building a Reinforcement Learning Environment from Limited Data to Optimize Teachable Robot Interventions
Building a Reinforcement Learning Environment from Limited Data to Optimize Teachable Robot Interventions
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发表时间：
2022
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通讯作者：
Tristan D. Maidment;Mingzhi Yu;Nikki G. Lobczowski;Adriana Kovashka;Erin Walker;D. Litman;Timothy J. Nokes-Malach
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作者：
Tristan D. Maidment;Mingzhi Yu;Nikki G. Lobczowski;Adriana Kovashka;Erin Walker;D. Litman;Timothy J. Nokes-Malach
团队协作可以对学生的表现和参与度产生积极影响。智能社会代理可以为学生提供个性化支持的来源，它们的好处可能扩展到协作环境，但很难确定这些代理应该如何与学生互动。强化学习（RL）为适应社会主体和学生之间的互动提供了一个机会，以更好地支持协作和学习。然而，在社会代理人的教育中使用强化学习通常涉及使用真实学生进行训练。在这项工作中，我们在高质量的模拟环境中训练了一个RL agent来学习如何提高学生的协作能力。数据是在一项试点研究中收集的，研究对象是一群学生，他们一起指导一个智能的可教机器人。我们从数据、培训政策以及政策对不同学生的影响来探索构建环境的过程，并与各种基线进行比较。
Working collaboratively in groups can positively impact performance and student engagement. Intelligent social agents can provide a source of personalized support for students, and their benefits likely extend to collaborative settings, but it is difficult to determine how these agents should interact with students. Reinforcement learning (RL) offers an opportunity for adapting the interactions between the social agent and the students to better support collaboration and learning. However, using RL in education with social agents typically involves training using real students. In this work, we train an RL agent in a high-quality simulated environment to learn how to improve students’ collaboration. Data was collected during a pilot study with dyads of students who worked together to tutor an intelligent teachable robot. We explore the process of building an environment from the data, training a policy, and the impact of the policy on different students, compared to various baselines.