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基于Multi-Agent深度强化学习的高效大规模车队管理
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: |
通过重新分配交通资源以缓解交通拥堵并提高交通效率，大型在线乘车共享平台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，是一项具有挑战性的任务。现有的研究通常在简化的问题设置上进行，难以捕捉高维空间中复杂的随机供需变化。在本文中，我们提出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agent强化学习框架，包括两个具体算法，即上下文深度Q学习和上下文多agent角色-评论家，以实现适应不同上下文的大量agent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，所提出的框架有显著改进。
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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: |
重新分配; 运输资源; 显式协调
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| 课程来源: |
视频讲座网
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| 数据采集: |
2022-12-12：chenjy
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| 最后编审: |
2022-12-12：chenjy
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| 阅读次数: |
31
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