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Title: TrajGAIL: Trajectory Generative Adversarial Imitation Learning for Long-Term Decision Analysis
Mobile sensing and information technology have enabled us to collect a large amount of mobility data from human decision-makers, for example, GPS trajectories from taxis, Uber cars, and passenger trip data of taking buses and trains. Understanding and learning human decision-making strategies from such data can potentially promote individual's well-being and improve the transportation service quality. Existing works on human strategy learning, such as inverse reinforcement learning, all model the decision-making process as a Markov decision process, thus assuming the Markov property. In this work, we show that such Markov property does not hold in real-world human decision-making processes. To tackle this challenge, we develop a Trajectory Generative Adversarial Imitation Learning (TrajGAIL) framework. It captures the long-term decision dependency by modeling the human decision processes as variable length Markov decision processes (VLMDPs), and designs a deep-neural-network-based framework to inversely learn the decision-making strategy from the human agent's historical dataset. We validate our framework using two real world human-generated spatial-temporal datasets including taxi driver passenger-seeking decision data and public transit trip data. Results demonstrate significant accuracy improvement in learning human decision-making strategies, when comparing to baselines with Markov property assumptions.  more » « less
Award ID(s):
1942680 1952085 1831140
NSF-PAR ID:
10225176
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2020 IEEE International Conference on Data Mining (ICDM)
Page Range / eLocation ID:
801 to 810
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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