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.
CITATION STYLE
Ruiz, S., & Hernández, B. (2019). A Hybrid Reinforcement Learning and Cellular Automata Model for Crowd Simulation on the GPU. In Communications in Computer and Information Science (Vol. 979, pp. 59–74). Springer Verlag. https://doi.org/10.1007/978-3-030-16205-4_5
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