Metis: Multi-Agent Based Crisis Simulation System

09/08/2020
by   George Sidiropoulos, et al.
0

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.

READ FULL TEXT

page 4

page 5

page 7

research
06/01/2020

Crowd simulation for crisis management: the outcomes of the last decade

The last few decades, crowd simulation for crisis management is highligh...
research
01/18/2022

K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents

Traditionally, the performance of multi-agent deep reinforcement learnin...
research
08/16/2011

Comparing System Dynamics and Agent-Based Simulation for Tumour Growth and its Interactions with Effector Cells

There is little research concerning comparisons and combination of Syste...
research
10/10/2020

HAMLET: A Hierarchical Agent-based Machine Learning Platform

Hierarchical Multi-Agent Systems provide a convenient and relevant way t...
research
11/24/2020

Modeling skier behavior for planning and management. Dynaski, an agent-based in congested ski-areas

In leisure spaces, particularly theme parks and museums, researchers and...
research
07/03/2023

Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning

Multi-Agent Reinforcement Learning (MARL) is a promising candidate for r...
research
07/17/2021

Megaverse: Simulating Embodied Agents at One Million Experiences per Second

We present Megaverse, a new 3D simulation platform for reinforcement lea...

Please sign up or login with your details

Forgot password? Click here to reset