<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning"^^ . "Autonomous unmanned aerial vehicles (UAVs) offer cost-effective and flexible solutions for a wide range of real-world applications, particularly in hazardous and time-critical environments. Their ability to navigate autonomously, communicate rapidly, and avoid collisions makes UAVs well suited for emergency response scenarios. However, real-time path planning in dynamic and unpredictable environments remains a major challenge, especially in confined tunnel infrastructures where accidents may trigger fires, smoke propagation, debris, and rapid environmental changes. In such conditions, conventional preplanned or model-based navigation approaches often fail due to limited visibility, narrow passages, and the absence of reliable localization signals. To address these challenges, this work proposes an end-to-end emergency response framework for tunnel accidents based on Multi-Agent Reinforcement Learning (MARL). Each UAV operates as an independent learning agent using an Independent Q-Learning paradigm, enabling real-time decision-making under limited computational resources. To mitigate premature convergence and local optima during exploration, Grey Wolf Optimization (GWO) is integrated as a policy-guidance mechanism within the reinforcement learning (RL) framework. A customized reward function is designed to prioritize victim discovery, penalize unsafe behavior, and explicitly discourage redundant exploration among agents. The proposed approach is evaluated using a frontier-based exploration simulator under both single-agent and multi-agent settings with multiple goals. Extensive simulation results demonstrate that the proposed framework achieves faster goal discovery, improved map coverage, and reduced rescue time compared to state-of-the-art GWO-based exploration and random search algorithms. These results highlight the effectiveness of lightweight MARL-based coordination for autonomous UAV-assisted tunnel emergency response."^^ . "2026-01" . . . . "Scientific Reports"^^ . . . "20452322" . . . . . . . . . . . . . . . . . . . . . . . . . "Yini Airet"^^ . "Miró Vera"^^ . "Yini Airet Miró Vera"^^ . . "Roberto Marcelo"^^ . "Álvarez"^^ . "Roberto Marcelo Álvarez"^^ . . "M. Junaid"^^ . "Gul"^^ . "M. Junaid Gul"^^ . . "Muhammad Zeeshan"^^ . "Jhandir"^^ . "Muhammad Zeeshan Jhandir"^^ . . "Rabbiya"^^ . "Younas"^^ . "Rabbiya Younas"^^ . . "Hafiz Muhammad Raza"^^ . "ur Rehman"^^ . "Hafiz Muhammad Raza ur Rehman"^^ . . "Imran"^^ . "Ashraf"^^ . "Imran Ashraf"^^ . . . . . . "End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning (Texto)"^^ . . . "s41598-026-37191-w_reference.pdf"^^ . . . "End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning (Otro)"^^ . . . . . . "indexcodes.txt"^^ . . "HTML Summary of #27154 \n\nEnd-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning\n\n" . "text/html" . . . "Engineering"@en . "Ingeniería"@es . .