Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling

• URL: http://arxiv.org/abs/2007.03155v2
• Date: Fri, 1 Dec 2023 14:19:31 GMT
• Title: Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling
• Authors: Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda
• Abstract summary: We propose sequential generative models with partial observation and mechanical constraints in a decentralized manner. Our approach can be used as a multi-agent simulator to generate realistic trajectories using real-world data.
• Score: 14.00358511581803
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents independently have limited observation and mechanical constraints; however, most of the conventional data-driven models ignore such assumptions, resulting in lack of biological plausibility and model interpretability for behavioral analyses. Here we propose sequential generative models with partial observation and mechanical constraints in a decentralized manner, which can model agents' cognition and body dynamics, and predict biologically plausible behaviors. We formulate this as a decentralized multi-agent imitation-learning problem, leveraging binary partial observation and decentralized policy models based on hierarchical variational recurrent neural networks with physical and biomechanical penalties. Using real-world basketball and soccer datasets, we show the effectiveness of our method in terms of the constraint violations, long-term trajectory prediction, and partial observation. Our approach can be used as a multi-agent simulator to generate realistic trajectories using real-world data.

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