# Emotional Contagion-Aware Deep Reinforcement Learning for Antagonistic Crowd Simulation

arXiv: https://arxiv.org/abs/2105.00854 · HTML: https://mlsanity.com/paper/2105.00854/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Heter-Sim: Heterogeneous multi-agent systems simulation by interactive data-driven optimization](https://mlsanity.com/paper/1812.00307/) (2018)
- [Crowd Behavior Simulation with Emotional Contagion in Unexpected Multi-hazard Situations](https://mlsanity.com/paper/1801.10000/) (2018)
- [From Public Outrage to the Burst of Public Violence: An Epidemic-Like Model](https://mlsanity.com/paper/1310.0731/) (2013)
- [ACSEE: Antagonistic Crowd Simulation Model with Emotional Contagion and Evolutionary Game Theory](https://mlsanity.com/paper/1902.00380/) (2019)

## Built on by (most cited first, 1 of 1)

- [Reward Function Design for Crowd Simulation via Reinforcement Learning](https://mlsanity.com/paper/2309.12841/) (2023)

## Abstract (arXiv)

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.