Environment Evasion Modeling in Emergency Situations: Computational Simulator with Reinforcement Learning

Resumo

The evacuation of environments during emergency situations is a crucial task in scenarios such as natural disasters, fires, and urban evacuations. This article presents an innovative approach based on advanced artificial intelligence techniques, with an emphasis on reinforcement learning. This methodology enables agents to learn how to make sequential decisions in uncertain environments by receiving feedback in the form of rewards or penalties. In evacuation contexts, the goal is for agents to autonomously and safely navigate dynamic and potentially hazardous environments. Reinforcement learning offers a versatile framework for training agents capable of avoiding obstacles, identifying safe routes, and maximizing the survival of themselves and others. The study discusses the theoretical foundations of reinforcement learning - including policies, rewards, and Markov decision processes - and their practical application to emergency evacuation modeling, highlighting the importance of realistic simulated environments and well-defined reward systems. Case studies and experimental results are presented, demonstrating the effectiveness of the proposed approach in simulated scenarios. These experiments show the agents' ability to overcome complex obstacles, adapt to sudden changes, and find safe evacuation routes in a timely manner. This study underscores the potential of reinforcement learning as a powerful tool to enhance the safety and efficiency of evacuation operations in critical situations.