Key points are not available for this paper at this time.
Reinforcement Learning (RL) has become a popular framework for learning desired behaviors for computational agents in graphics and games. In a multi-agent crowd, one major goal is for agents to avoid collisions while navigating in a dynamic environment. Another goal is to simulate natural-looking crowds, which is difficult to define due to the ambiguity as to what is a natural crowd motion. We introduce a novel methodology for simulating crowds, which learns most-preferred crowd simulation behaviors from crowd-sourced votes via Bayesian optimization. Our method uses deep reinforcement learning for simulating crowds, where crowdsourcing is used to select policy hyper-parameters. Training agents with such parameters results in a crowd simulation that is preferred to users. We demonstrate our method's robustness in multiple scenarios and metrics, where we show it is superior compared to alternate policies and prior work.
Building similarity graph...
Analyzing shared references across papers
Loading...
Talukdar et al. (Sat,) studied this question.
www.synapsesocial.com/papers/68e6a89ab6db64358762bdfe — DOI: https://doi.org/10.1145/3651302
B. Talukdar
Yunhao Zhang
Tomer Weiss
Proceedings of the ACM on Computer Graphics and Interactive Techniques
New Jersey Institute of Technology
Building similarity graph...
Analyzing shared references across papers
Loading...