  The antagonistic behavior in the crowd usually exacerbates the seriousness of
the situation in sudden riots, where the antagonistic emotional contagion and
behavioral decision making play very important roles. However, the complex
mechanism of antagonistic emotion influencing decision making, especially in
the environment of sudden confrontation, has not yet been explored very
clearly. In this paper, we propose an Emotional contagion-aware Deep
reinforcement learning model for Antagonistic Crowd Simulation (ACSED).
Firstly, we build a group emotional contagion module based on the improved
Susceptible Infected Susceptible (SIS) infection disease model, and estimate
the emotional state of the group at each time step during the simulation. Then,
the tendency of crowd antagonistic action is estimated based on Deep Q Network
(DQN), where the agent learns the action autonomously, and leverages the mean
field theory to quickly calculate the influence of other surrounding
individuals on the central one. Finally, the rationality of the predicted
actions by DQN is further analyzed in combination with group emotion, and the
final action of the agent is determined. The proposed method in this paper is
verified through several experiments with different settings. The results prove
that the antagonistic emotion has a vital impact on the group combat, and
positive emotional states are more conducive to combat. Moreover, by comparing
the simulation results with real scenes, the feasibility of our method is
further confirmed, which can provide good reference to formulate battle plans
and improve the win rate of righteous groups in a variety of situations.
