104 citations · 110 across the 6 of their papers we have counts for
6 papers
Reward Function Design for Crowd Simulation via Reinforcement Learning
Ariel Kwiatkowski, Vicky Kalogeiton, Julien Pettré +1
Crowd simulation is important for video-games design, since it enables to populate virtual worlds with autonomous avatars that navigate in a human-like manner. Reinforcement learni…
Properties of pedestrians walking in line without density constraint
Cécile Appert-Rolland, Anne-Hélène Olivier, Julien Pettré
This article deals with the experimental study of pedestrian behaviours in some situations of one-dimensional traffic. Participants were pre-organized in a line, and asked to walk…
UGAE: A Novel Approach to Non-exponential Discounting
Ariel Kwiatkowski, Vicky Kalogeiton, Julien Pettré +1
The discounting mechanism in Reinforcement Learning determines the relative importance of future and present rewards. While exponential discounting is widely used in practice, non-…
Methods of density estimation for pedestrians moving in small groups without a spatial boundary
Pratik Mullick, Cécile Appert-Rolland, William H. Warren +1
For a group of pedestrians without any spatial boundaries, the methods of density estimation is a wide area of research. Besides, there is a specific difficulty when the density al…
Understanding reinforcement learned crowds
Ariel Kwiatkowski, Vicky Kalogeiton, Julien Pettré +1
Simulating trajectories of virtual crowds is a commonly encountered task in Computer Graphics. Several recent works have applied Reinforcement Learning methods to animate virtual a…
A hierarchy of heuristic-based models of crowd dynamics
Pierre Degond, Cécile Appert-Rolland, Mehdi Moussaid +2
We derive a hierarchy of kinetic and macroscopic models from a noisy variant of the heuristic behavioral Individual-Based Model of Moussaid et al, PNAS 2011, where the pedestrians…