“Learning To Move In Crowd” by Lee and Lee
Conference:
Type:
Entry Number: 12
Title:
- Learning To Move In Crowd
Presenter(s)/Author(s):
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:
- 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
- Jan Ondrej, Julien Pettre, Anne-Helene Olivier, and Stephane Donikian. 2010. A Synthetic-vision Based Steering Approach for Crowd Simulation. ACM Trans. Graph. 29, 4, Article 123 (July 2010), 9 pages. DOI:http://dx.doi.org/10.1145/1778765. 1778860
Keyword(s):
Acknowledgements:
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the SW Starlab support program(IITP-2017-0- 00878) supervised by the IITP(Institute for Information & communications Technology Promotion).
