FLDQN:合作多代理联合增强学习,用于解决动态环境中的旅行时间最小化问题,使用SUMO模拟
Abdul Wahab Mamond1, Majid Kundroo1, Seong-Eun Yoo2
1School of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了FLDQN,一个合作的多代理联合强化学习算法. 通过使智能代理人在动态交通环境中分享知识和协作,FLDQN显著减少了旅行时间和拥堵.
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:
- 越来越多的交通量导致拥堵,污染和更长的旅行时间.
- 深度增强学习 (DRL) 在交通管理方面表现有前途,但仅限于单个代理系统.
- 合作多代理强化学习 (MARL) 由于代理管理和协作复杂性而具有挑战性.
研究的目的:
- 引入一个合作的多代理联合强化学习算法 (FLDQN) 以优化道路网络利用.
- 在动态MARL场景中应对代理合作的挑战.
- 为了尽量减少旅行时间和减少交通拥堵.
主要方法:
- 开发了FLDQN,这是一个用于合作MARL的联合强化学习算法.
- 使用SUMO模拟器进行多代理互动和环境建模.
- 代理人使用深度Q学习,通过联合服务器共享模型更新,以加强集体政策.
主要成果:
- 与非合作方法相比,FLDQN的平均旅行时间减少了34.6%以上.
- 显著减少了交通拥堵.
- 通过分布式学习降低了计算开销.
结论:
- 在多代理系统中,代理合作对于有效的流量管理至关重要.
- FLDQN提供了一种创新的解决方案,使智能代理之间的合作和知识共享成为可能.
- 联合学习通过在动态环境中利用集体经验,促进了政策学习的增强.
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