  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.
