Metis: Multi-Agent Based Crisis Simulation System

TL;DR

This paper introduces Metis, a user-friendly multi-agent crisis simulation system for indoor environments that integrates reinforcement learning, realistic physics, and GPU acceleration to enhance crisis management and evacuation planning.

Contribution

It presents a novel, adaptable simulation platform designed for non-expert users, combining environment customization, reinforcement learning integration, and physics-based modeling.

Findings

Demonstrated effective agent training with reinforcement learning algorithms.

Showcased realistic fire propagation and physics-based environment modeling.

Validated system usability through a practical case study.

Abstract

With the advent of the computational technologies (Graphics Processing Units - GPUs) and Machine Learning, the research domain of crowd simulation for crisis management has flourished. Along with the new techniques and methodologies that have been proposed all those years, aiming to increase the realism of crowd simulation, several crisis simulation systems/tools have been developed, but most of them focus on special cases without providing users the ability to adapt them based on their needs. Towards these directions, in this paper, we introduce a novel multi-agent-based crisis simulation system for indoor cases. The main advantage of the system is its ease of use feature, focusing on non-expert users (users with little to no programming skills) that can exploit its capabilities a, adapt the entire environment based on their needs (Case studies) and set up building evacuation planning experiments with some of the most popular Reinforcement Learning algorithms. Simply put, the system's features focus on dynamic environment design and crisis management, interconnection with popular Reinforcement Learning libraries, agents with different characteristics (behaviors), fire propagation parameterization, realistic physics based on popular game engine, GPU-accelerated agents training and simulation end conditions. A case study exploiting a popular reinforcement learning algorithm, for training of the agents, presents the dynamics and the capabilities of the proposed systems and the paper is concluded with the highlights of the system and some future directions.