Analysing Congestion Problems in Multi-agent Reinforcement Learning

被引：0

作者：

Radulescu, Roxana ^{[1
]}

Vrancx, Peter ^{[1
]}

Nowe, Ann ^{[1
]}

机构：

[1] Vrije Univ Brussel, Brussels, Belgium

来源：

AAMAS'17: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS | 2017年

关键词：

Multi-agent reinforcement learning; Congestion problems; Resource abstraction;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We extend the study of congestion problems to a more realistic scenario, the Road Network Domain (RND), where the resources are no longer independent, but rather part of a network, thus choosing one path will also impact the load of another one having common road segments. We demonstrate the application of state-of-the-art multi-agent reinforcement learning methods for this new congestion model and analyse their performance. RND allows us to highlight an important limitation of resource abstraction and show that the difference rewards approach manages to better capture and inform the agents about the dynamics of the environment.

引用

页码：1705 / 1707

页数：3

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