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This thesis presents a novel fire evacuation guidance framework utilizing a parameter sharing based adaptive multi-agent reinforcement learning algorithm, specifically APF-DQN, which integrates artificial potential fields for real-time path planning in dynamic fire environments. The proposed PS-MAPPO algorithm enhances evacuation efficiency by addressing challenges in multi-agent scenarios through federated learning techniques, resulting in improved success rates and reduced evacuation times. The research i

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b8zg2vpjds

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EN