Multi-agent Reinforcement Learning for Collaborative Transportation Management (CTM)

被引：7

作者：

Okdinawati, Liane ^{[1
]}

Simatupang, Togar M. ^{[1
]}

Sunitiyoso, Yos ^{[1
]}

机构：

[1] Bandung Inst Technol, Sch Business & Management, Bandung, Indonesia

来源：

AGENT-BASED APPROACHES IN ECONOMICS AND SOCIAL COMPLEX SYSTEMS IX | 2017年 / 15卷

关键词：

Multi-agent system; Agent-based modelling; Reinforcement learning; Collaborative Transportation Management (CTM);

D O I：

10.1007/978-981-10-3662-0_10

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Collaborative Transportation Management (CTM) is a model collaboration in transportation area conducted through information and resources sharing. Planning and implementing CTM not only involve optimization of decisions for all collaborative agents but also involve the influence of different interactions among agents to achieve higher CTM benefits. This paper explores how agent-based modelling is used to model interaction and learning process in CTM in real systems. Agent-based model is used in this paper based on consideration that agent-based model can model the emergent decision patterns and unexpected changes of decision based on the decision-making structure. Model-free reinforcement learning is used to predict the consequences and optimize all agents' action in CTM.

引用

页码：123 / 136

页数：14

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