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Intelligent Traffic Light Control
- Publisher
- Association for Computing Machinery
- Copyright
- Copyright © 2012 by ACM Inc.
- ISSN
- 1556-4665
- DOI
- 10.1145/2168260.2168271
- Publisher site
- See Article on Publisher Site
Abstract
Autonomic Multi-Policy Optimization in Pervasive Systems: Overview and Evaluation IVANA DUSPARIC and VINNY CAHILL, Trinity College Dublin This article describes Distributed W-Learning (DWL), a reinforcement learning-based algorithm for collaborative agent-based optimization of pervasive systems. DWL supports optimization towards multiple heterogeneous policies and addresses the challenges arising from the heterogeneity of the agents that are charged with implementing them. DWL learns and exploits the dependencies between agents and between policies to improve overall system performance. Instead of always executing the locally-best action, agents learn how their actions affect their immediate neighbors and execute actions suggested by neighboring agents if their importance exceeds the local action ™s importance when scaled using a prede ned or learned collaboration coef cient. We have evaluated DWL in a simulation of an Urban Traf c Control (UTC) system, a canonical example of the large-scale pervasive systems that we are addressing. We show that DWL outperforms widely deployed xed-time and simple adaptive UTC controllers under a variety of traf c loads and patterns. Our results also con rm that enabling collaboration between agents is bene cial as is the ability for agents to learn the degree to which it is appropriate for them to collaborate.
Journal
ACM Transactions on Autonomous and Adaptive Systems (TAAS) – Association for Computing Machinery
Published: Apr 1, 2012