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A Hybrid Reinforcement Learning and Cellular Automata Model for Crowd Simulation on the GPU

  • Sergio RuizEmail author
  • Benjamín Hernández
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 979)

Abstract

We present a GPU-based hybrid model for crowd simulations. The model uses reinforcement learning to guide groups of pedestrians towards a goal while adapting to environmental dynamics, and a cellular automaton to describe individual pedestrians’ interactions. In contrast to traditional multi-agent reinforcement learning methods, our model encodes the learned navigation policy into a navigation map, which is used by the cellular automaton’s update rule to calculate the next simulation step. As a result, reinforcement learning is independent of the number of agents, allowing the simulation of large crowds. Implementation of this model on the GPU allows interactive simulations of several hundreds of pedestrians.

Keywords

Reinforcement learning Crowd simulation Cellular automata GPU 

Notes

Acknowledgements

This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. We thank NVIDIA for the donation of the Titan X GPU used in this research. Sergio Ruiz would like to thank the Tecnologico de Monterrey Computer Department for its support.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tecnológico de MonterreyMexico CityMexico
  2. 2.Oak Ridge National LaboratoryOak RidgeUSA

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