@inproceedings{604a399c9afc4f39856b0f02b1d20d37,
title = "Is Reinforcement Learning the Choice of Human Learners?: A Case Study of Taxi Drivers",
abstract = "Learning to make optimal decisions is a common yet complicated task. While computer agents can learn to make decisions by running reinforcement learning (RL), it remains unclear how human beings learn. In this paper, we perform the first data-driven case study on taxi drivers to validate whether humans mimic RL to learn. We categorize drivers into three groups based on their performance trends and analyze the correlations between human drivers and agents trained using RL. We discover that drivers that become more efficient at earning over time exhibit similar learning patterns to those of agents, whereas drivers that become less efficient tend to do the opposite. Our study (1) provides evidence that some human drivers do adapt RL when learning, (2) enhances the deep understanding of taxi drivers' learning strategies, (3) offers a guideline for taxi drivers to improve their earnings, and (4) develops a generic analytical framework to study and validate human learning strategies.",
keywords = "human learning strategy, reinforcement learning, urban computing",
author = "Menghai Pan and Weixiao Huang and Yanhua Li and Xun Zhou and Zhenming Liu and Jie Bao and Yu Zheng and Jun Luo",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020 ; Conference date: 03-11-2020 Through 06-11-2020",
year = "2020",
month = nov,
day = "3",
doi = "10.1145/3397536.3422246",
language = "英语",
series = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems",
publisher = "Association for Computing Machinery ",
pages = "357--366",
editor = "Chang-Tien Lu and Fusheng Wang and Goce Trajcevski and Yan Huang and Shawn Newsam and Li Xiong",
booktitle = "Proceedings of the 28th International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020",
address = "美国",
}