Learning to Wait: Preventing Global Congestion from Local Observations in Real-Time Crowd Navigation

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dc.contributor.authorRuprecht, Irenaen_US
dc.contributor.authorMichelic, Florianen_US
dc.contributor.authorPreiner, Reinholden_US
dc.contributor.editorComino Trinidad, Marcen_US
dc.contributor.editorMancinelli, Claudioen_US
dc.contributor.editorMaggioli, Filippoen_US
dc.contributor.editorRomanengo, Chiaraen_US
dc.contributor.editorCabiddu, Danielaen_US
dc.contributor.editorGiorgi, Danielaen_US
dc.date.accessioned2025-11-21T07:28:48Z
dc.date.available2025-11-21T07:28:48Z
dc.date.issued2025
dc.description.abstractWe present a real-time crowd simulation approach based on reinforcement learning (RL), addressing congestion prevention in confined spaces. We learn a local navigation policy that uses compact, fast-to-compute per-agent observations of a small set of neighbors, including their desired directions. Alongside goal progress and inter-agent spacing, we reward agents for waiting when neighbors ahead pursue similar goals. This formulation fosters global self-organization from purely local interactions. Preliminary results show reduced congestion and consistent goal attainment for large crowds with hundreds of agents.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20251341
dc.identifier.isbn978-3-03868-296-7
dc.identifier.issn2617-4855
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20251341
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20251341
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Real-time simulation; Multi-agent reinforcement learning
dc.subjectComputing methodologies → Real
dc.subjecttime simulation
dc.subjectMulti
dc.subjectagent reinforcement learning
dc.titleLearning to Wait: Preventing Global Congestion from Local Observations in Real-Time Crowd Navigationen_US
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