Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/330006 
Year of Publication: 
2023
Citation: 
[Journal:] Games [ISSN:] 2073-4336 [Volume:] 14 [Issue:] 1 [Article No.:] 13 [Year:] 2023 [Pages:] 1-13
Publisher: 
MDPI, Basel
Abstract: 
How to sample training/validation data is an important question for machine learning models, especially when the dataset is heterogeneous and skewed. In this paper, we propose a data sampling method that robustly selects training/validation data. We formulate the training/validation data sampling process as a two-player game: a trainer aims to sample training data so as to minimize the test error, while a validator adversarially samples validation data that can increase the test error. Robust sampling is achieved at the game equilibrium. To accelerate the searching process, we adopt reinforcement learning aided Monte Carlo trees search (MCTS). We apply our method to a car-following modeling problem, a complicated scenario with heterogeneous and random human driving behavior. Real-world data, the Next Generation SIMulation (NGSIM), is used to validate this method, and experiment results demonstrate the sampling robustness and thereby the model out-of-sample performance.
Subjects: 
two-player game
Monte Carlo tree search
reinforcement learning
car-following modeling
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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