基于P-MADDPG的突发事件决策
Decision-making of Emergent Incident based on P-MADDPG
摘要：
近年来，突发事件造成的人员伤亡和资源破坏已成为世界性的严重问题。本文对突发事件应急决策问题进行了建模，并利用多智能体系统（MAS）解决了决策速度跟不上传播速度的问题。MAS可以在这些任务的自动执行中发挥重要作用，以减少任务完成时间。针对突发事件的应急决策问题，提出了一种P-MADDPG算法，该算法通过GRU模型预测下一时刻可能发生事件的节点，并在事件发生前做出决策，解决了决策速度跟不上传播速度的问题。建立了真实场景的仿真环境，选取无人仓储、工厂流水线和民用机场行李运输3种场景，对P-MADDPG在突发事件应急决策问题中的性能进行了测试。仿真结果表明，P-MADDPG算法与贪心算法和MADDPG算法相比，在不同规模的场景下，P-MADDPG算法收敛速度更快，收敛效果更好。这表明P-MADDP算法在突发事件应急决策中是有效的。
英文摘要：
In recent years, human casualties and damage to resources caused by emergent incidents have become a serious problem worldwide. In this paper, we model the emergency decision-making problem and use Multi-agent System (MAS) to solve the problem that the decision speed cannot keep up with the spreading speed. MAS can play an important role in the automated execution of these tasks to reduce mission completion time. In this paper, we propose a P-MADDPG algorithm to solve the emergency decision-making problem of emergent incidents, which predicts the nodes where an incident may occur in the next time by GRU model and makes decisions before the incident occurs, thus solving the problem that the decision speed cannot keep up with the spreading speed. A simulation environment was established for realistic scenarios, and three scenarios were selected to test the performance of P-MADDPG in emergency decision-making problems for emergent incidents: unmanned storage, factory assembly line, and civil airport baggage transportation. Simulation results using the P-MADDPG algorithm are compared with the greedy algorithm and the MADDPG algorithm, and the final experimental results show that the P-MADDPG algorithm converges faster and better than the other algorithms in scenarios of different sizes. This shows that the P-MADDP algorithm is effective for emergency decision-making in emergent incident.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
