Policy heterogeneity improves collective olfactory search in 3-D turbulence
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866914441033940992 |
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| author | Piro, Lorenzo Heinonen, Robin A. Carbone, Maurizio Biferale, Luca Cencini, Massimo |
| author_facet | Piro, Lorenzo Heinonen, Robin A. Carbone, Maurizio Biferale, Luca Cencini, Massimo |
| contents | We investigate the role of policy heterogeneity in enhancing the olfactory search capabilities of cooperative agent swarms operating in complex, real-world turbulent environments. Using odor fields from direct numerical simulations of the Navier-Stokes equations, we demonstrate that heterogeneous groups, with exploratory and exploitative agents, consistently outperform homogeneous swarms where the exploration-exploitation tradeoff is managed at the individual level. Our results reveal that policy diversity enables the group to reach the odor source more efficiently by mitigating the detrimental effects of spatial correlations in the signal. These findings provide new insights into collective search behavior in biological systems and offer promising strategies for the design of robust, bioinspired search algorithms in engineered systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Policy heterogeneity improves collective olfactory search in 3-D turbulence Piro, Lorenzo Heinonen, Robin A. Carbone, Maurizio Biferale, Luca Cencini, Massimo Fluid Dynamics Computational Physics Data Analysis, Statistics and Probability We investigate the role of policy heterogeneity in enhancing the olfactory search capabilities of cooperative agent swarms operating in complex, real-world turbulent environments. Using odor fields from direct numerical simulations of the Navier-Stokes equations, we demonstrate that heterogeneous groups, with exploratory and exploitative agents, consistently outperform homogeneous swarms where the exploration-exploitation tradeoff is managed at the individual level. Our results reveal that policy diversity enables the group to reach the odor source more efficiently by mitigating the detrimental effects of spatial correlations in the signal. These findings provide new insights into collective search behavior in biological systems and offer promising strategies for the design of robust, bioinspired search algorithms in engineered systems. |
| title | Policy heterogeneity improves collective olfactory search in 3-D turbulence |
| topic | Fluid Dynamics Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2504.11291 |