Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners

• URL: http://arxiv.org/abs/2009.02476v4
• Date: Thu, 29 Jun 2023 04:40:18 GMT
• Title: Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
• Authors: Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe Ma, Mark K. Ho, Joseph L. Austerweil, Xiaojin Zhu
• Abstract summary: We focus on a common reinforcement learning method, Q-learning, and examine what assumptions people have using a behavioral experiment. We use a deep learning approximation method which simulates learners in the environment and learns to predict how feedback affects the learner's internal states. Our results reveal how people teach using evaluative feedback and provide guidance for how engineers should design machine agents in a manner that is intuitive for people.
• Score: 26.006964607579004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Successful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to update their internal states. We investigate what expectations people have about a learner when they teach them in an online manner using rewards and punishment. We focus on a common reinforcement learning method, Q-learning, and examine what assumptions people have using a behavioral experiment. To do so, we first establish a normative standard, by formulating the problem as a machine teaching optimization problem. To solve the machine teaching optimization problem, we use a deep learning approximation method which simulates learners in the environment and learns to predict how feedback affects the learner's internal states. What do people assume about a learner's learning and discount rates when they teach them an idealized exploration-exploitation task? In a behavioral experiment, we find that people can teach the task to Q-learners in a relatively efficient and effective manner when the learner uses a small value for its discounting rate and a large value for its learning rate. However, they still are suboptimal. We also find that providing people with real-time updates of how possible feedback would affect the Q-learner's internal states weakly helps them teach. Our results reveal how people teach using evaluative feedback and provide guidance for how engineers should design machine agents in a manner that is intuitive for people.

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