通过使用持续学习,提高高速公路变速限制控制策略的多场景适用性
Ruici Zhang1, Shoulong Xu1, Rongjie Yu1
1College of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao'an Road, 201804 Shanghai, China.
Accident; analysis and prevention
|June 5, 2024
概括
对于变速限制 (VSL) 控制的深度强化学习 (DRL) 与场景忘记作斗争. 使用渐变投影存储器 (GPM) 的新持续学习方法有效地保留了过去的学习,提高了VSL策略的性能.
科学领域:
- 智能运输系统 智能运输系统
- 机器学习 机器学习
- 控制理论 控制理论
背景情况:
- 可变速度限制 (VSL) 系统通过动态调整速度限制以应对交通拥堵和事故预防等场景,来提高高速公路运行.
- 深度强化学习 (DRL) 用于通过将交通条件映射到速度限制来开发VSL策略.
- 在多场景应用中,DLR面临性能下降的原因是"场景遗忘",在新场景训练中,以前学习的信息会丢失.
研究的目的:
- 引入持续学习方法,以提高VSL控制策略的多场景适用性.
- 为了解决基于DRL的VSL系统中的"忘记场景"问题.
- 增强VSL控制策略在各种交通条件中的稳定性和通用性.
主要方法:
- 提出了一种基于梯度投影内存 (GPM) 的神经网络参数更新方法.
- 这种方法限制了梯度更新,以在新情景训练期间保留从以前的场景中学到的记忆.
- 该方法使用在SUMO中模拟的三个高速公路运行场景进行了评估.
主要成果:
- 持续学习方法在以前训练的场景中减少了17.76%的绩效下降.
- 多场景VSL控制策略使速度标准偏差降低了28.77%,平均行程时间降低了7.25%.
- 评估了基于持续学习的VSL方法的概括能力.
结论:
- 持续学习有效地减轻了基于DRL的VSL系统中的场景遗忘.
- 拟议的GPM方法提高了VSL策略在动态,多场景环境中的长期性能和适用性.
- 开发的VSL策略为交通流效率和安全提供了显著的改进.
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