Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models

This paper analyzes the emergent behaviors of pedestrian groups that learn through the multiagent reinforcement learning model developed in our group. Five scenarios studied in the pedestrian model literature, and with different levels of complexity, were simulated in order to analyze the robustness and the scalability of the model. Firstly, a reduced group of agents must learn by interaction with the environment in each scenario. In this phase, each agent learns its own kinematic controller, that will drive it at a simulation time. Secondly, the number of simulated agents is increased, in each scenario where agents have previously learnt, to test the appearance of emergent macroscopic behaviors without additional learning. This strategy allows us to evaluate the robustness and the consistency and quality of the learned behaviors. For this purpose several tools from pedestrian dynamics, such as fundamental diagrams and density maps, are used. The results reveal that the developed model is capable of simulating human-like micro and macro pedestrian behaviors for the simulation scenarios studied, including those where the number of pedestrians has been scaled by one order of magnitude with respect to the situation learned.This work has been supported by grant TIN2015-65686-C5-1-R of Ministerio de Economía y Competitividad

Agent-Based Simulation Of Crowd At The Tawaf Area.

Every year during the Hajj season there is a concentration of more than two million people within the vicinity of the Masjid Al-Haram. Congested areas, such as the tawaf,, may reach beyond a safe level of four people per square meter during this peak period. The Tawaf area together with the Ottoman construction is able to accommodate up to 72,000 people (in a praying position). Simulation of the movement and behavior of such a huge crowd can be useful in managing this important event. One of the recent trends in modeling and simulation is the agent technology which has been used to model and simulate various phenomenon such as the study of land use, infectious disease modeling, economic and business study, urban dynamic and also pedestrian modeling. In this paper we use multi-agent based method to simulate the crowd at the Tawaf area. We present the architecture of the software platform which implements our proposed model and briefly report our early experience in using- Repast J which is an agent-based simulation toolkit to model the crowd at the area

The Multi-agent Simulation-based Framework for Optimization of Detectors Layout in Public Crowded Places

AbstractIn this work the framework for detectors layout optimization based on a multi-agent simulation is proposed. Its main intention is to provide a decision support team with a tool for automatic design of social threat detection systems for public crowded places. Containing a number of distributed detectors, this system performs detection and an identification of threat carriers. The generic model of detector used in the framework allows to consider detection of various types of threats, e.g. infections, explosives, drugs, radiation. The underlying agent-based models provide data on social mobility, which is used along with a probability based quality assessment model within the optimization process. The implemented multi-criteria optimization scheme is based on a genetic algorithm. For experimental study the framework has been applied in order to get the optimal detectors’ layout in Pulkovo airport