"Learning to Simulate Crowds with Crowds" by Bilas Talukdar, Yunhao Zhang et al.
 

Learning to Simulate Crowds with Crowds

Document Type

Conference Proceeding

Publication Date

7-23-2023

Abstract

Controlling agent behaviors with Reinforcement Learning is of continuing interest in multiple areas. One major focus is to simulate multi-Agent crowds that avoid collisions while locomoting to their goals. Although avoiding collisions is important, it is also necessary to capture realistic anticipatory navigation behaviors. We introduce a novel methodology that includes: 1) an RL method for learning an optimal navigational policy, 2) position-based constraints for correcting policy navigational decisions, and 3) a crowd-sourcing framework for selecting policy control parameters. Based on optimally selected parameters, we train a multi-Agent navigation policy, which we demonstrate on crowd benchmarks. We compare our method to existing works, and demonstrate that our approach achieves superior multi-Agent behaviors.

Identifier

85167946538 (Scopus)

ISBN

[9798400701528]

Publication Title

Proceedings SIGGRAPH 2023 Posters

External Full Text Location

https://doi.org/10.1145/3588028.3603670

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