Policy learning with partial observation and mechanical constraints for multi-person modeling
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Extracting the rules of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents generally 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 in biological and cognitive science. Here we propose sequential generative models with partial observation and mechanical constraints, which can visualize whose information the agents utilize and can generate biologically plausible actions. We formulate this as a decentralized multi-agent imitation learning problem, leveraging binary partial observation models with a Gumbel-Softmax reparameterization and policy models based on hierarchical variational recurrent neural networks with physical and biomechanical constraints. We investigate the empirical performances using real-world multi-person motion datasets from basketball and soccer games.
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