EvacuAI:在室内环境中利用强化学习的帮助分析逃生路线
Anna Carolina Rosa1, Mariana Cabral Falqueiro1, Rodrigo Bonacin2
1Electrical Engineering Department, University of Brasilia, Brasilia 70910-900, DF, Brazil.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
深度强化学习为火灾期间快速疏散建筑物提供了可行的解决方案,其性能优于传统算法. 转移学习显著提高了代理人训练的速度和准确性,以获得最佳的逃生路线.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 应急管理 应急管理
背景情况:
- 由于反应时间有限,在火灾期间迅速撤离建筑物至关重要.
- 传统的路径算法在动态环境中面临着多目标问题的性能问题.
- 需要先进的技术解决方案来协助高效的建筑物疏散.
研究的目的:
- 研究深度强化学习 (DRL) 的应用,以优化建筑物疏散路线.
- 为了比较DRL策略与传统路径算法的性能.
- 评估转移学习在加速DRL特工疏散培训方面的有效性.
主要方法:
- 使用深度强化学习技术来解决多目标疏散路径的发现.
- 评估了诸如重播缓冲器的随机初始化和转移学习等策略.
- 提出的方法在三个不同规模的学校项目中进行了测试.
主要成果:
- 在快速疏散路线规划中,DRL方法被证明是可行的.
- 转移学习表现出卓越的性能,使代理人训练更快 (不到1分钟).
- 该系统在确定最佳疏散路线方面实现了100%的准确性.
结论:
- 深度强化学习是一种有前途的技术,可以提高建筑物的消防安全和疏散效率.
- 转移学习显著提高了实时应用程序DRL代理人的培训效率.
- 未来的研究应该解决大规模复杂疏散场景中实施DRL的挑战.
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