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通过多Agent深度强化学习实现高效的大规模车队管理
Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning |
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| 课程网址: |
http://videolectures.net/kdd2018_lin_fleet_management/
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| 主讲教师: |
Kaixiang Lin
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| 开课单位: |
密歇根州立大学
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| 开课时间: |
2018-11-23
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| 课程语种: |
英语
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| 中文简介: |
大规模的在线拼车平台通过重新分配交通资源，缓解交通拥堵，提高交通效率，极大地改变了我们的生活。高效的车队管理策略不仅可以显著提高运输资源的利用率，还可以提高收入和客户满意度。设计一种有效的车队管理策略，以适应需求和供应之间复杂动态的环境，这是一项具有挑战性的任务。现有的研究通常针对一个简化的问题集，该问题集很难捕捉高维空间中复杂的随机供求变化。在本文中，我们建议使用强化学习来解决大规模车队管理问题，并提出了一个上下文多智能体强化学习框架，该框架包括两种具体算法，即上下文深度Q学习和上下文多智能主体行动者-批评者，以实现大量自适应于不同上下文的智能体之间的显式协调。我们通过广泛的实证研究表明，与最先进的方法相比，拟议的框架有了显著的改进。
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| 课程简介: |
Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportation efficiency. An efficient fleet management strategy not only can significantly improve the utilization of transportation resources but also increase the revenue and customer satisfaction. It is a challenging task to design an effective fleet management strategy that can adapt to an environment involving complex dynamics between demand and supply. Existing studies usually work on a simplified problem setting that can hardly capture the complicated stochastic demand-supply variations in high-dimensional space. In this paper we propose to tackle the large-scale fleet management problem using reinforcement learning, and propose a contextual multi-agent reinforcement learning framework including two concrete algorithms, namely contextual deep Q-learning and contextual multi-agent actor-critic, to achieve explicit coordination among a large number of agents adaptive to different contexts. We show significant improvements of the proposed framework over state-of-the-art approaches through extensive empirical studies.
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| 关 键 词: |
在线拼车平台; 缓解交通拥堵; 多智能体强化学习框架; 上下文深度Q学习; 提高运输资源的利用率
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| 课程来源: |
视频讲座网
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| 数据采集: |
2023-03-27：cyh
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| 最后编审: |
2023-03-27：cyh
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| 阅读次数: |
22
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