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- W3204358081 abstract "Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions, which is essential for understanding physical, social, and team-play systems. However, most existing works on modeling multi-agent interactions typically assume that agents make independent decisions based on their observations, ignoring the complex dependence among agents. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents. Extensive experiments on synthetic and real-world datasets show that our model outperforms state-of-the-art baselines across various scenarios in the action prediction task, and is able to generate new trajectories close to expert demonstrations." @default.
- W3204358081 created "2021-10-11" @default.
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- W3204358081 date "2021-01-01" @default.
- W3204358081 modified "2023-10-16" @default.
- W3204358081 title "Multi-agent Imitation Learning with Copulas" @default.
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- W3204358081 doi "https://doi.org/10.1007/978-3-030-86486-6_9" @default.
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