Learning to move in crowd

ABSTRACT

The main goal of the crowd simulation is to generate realistic movements of agents. Reproducing the mechanism that seeing the environments, understanding current situation, and deciding where to step is crucial point to simulating crowd movements. We formulate the process of walking mechanism using deep reinforcement learning. And we experiment some typical scenarios.

References

1. Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. CoRR abs/1509.02971 (2015). http://arxiv.org/abs/1509.02971
2. Jan Ondřej, Julien Pettré, Anne-Hélène Olivier, and Stéphane Donikian. 2010. A Synthetic-vision Based Steering Approach for Crowd Simulation. ACM Trans. Graph. 29, 4, Article 123 (July 2010), 9 pages.

Index Terms

• Computing methodologies

  • Computer graphics

    • Animation

  • Machine learning

    • Learning paradigms

      • Reinforcement learning