基于深度强化学习的联合城市交通信号最佳控制
Mi Li1,2, Xiaolong Pan3, Chuhui Liu4
1College of Information Science and Engineering, Jiaxing University, Jiaxing, 314000, China. limi@zjxu.edu.cn.
Scientific reports
|April 5, 2025
概括
本研究介绍了用于智能交通信号控制的联合近接政策优化 (PPO) 方法,增强复杂交通网络中的学习速度和模型概括性,同时确保数据隐私.
科学领域:
- 人工智能的人工智能
- 运输工程 运输工程
- 分布式系统 分布式系统
背景情况:
- 深度强化学习 (RL) 在跨领域的交通信号控制方面面临挑战,包括缓慢的学习和糟糕的概括.
- 现有的方法在现实交通场景中与非独立和异质的环境数据作斗争.
研究的目的:
- 开发一个跨领域的智能交通信号控制方法,使用联合的近接政策优化 (PPO).
- 为了解决缓慢的学习速度和增强模型通用化,以优化交通信号.
- 在分布式联合培训期间确保信息安全和数据隐私.
主要方法:
- 提出了一个联合的PPO方法,用于在不同交通领域的代理人进行分布式联合培训.
- 设计了状态,动作和奖励功能,并优化了联合协作参数.
- 实施了一种新的状态交互方法和奖励功能,以改善代理合作和通信效率.
主要成果:
- 与固定时间相比,平均车辆等待时间减少了多达27.34%.
- 实现的融合速度高达比单个PPO快47.69%,比聚合PPO快45.35%.
- 在各种流量设置中表现出卓越的稳定性.
结论:
- 联合的PPO方法有效地优化了交叉路口的访问效率和交通流量.
- 这种方法提高了学习效率,快速融合,并在跨领域的交通控制中实现模型概括.
- 确保信息安全和数据隐私,同时提高交通系统联合学习的通信效率.
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