NDLI: Traffic light control in non-stationary environments based on multi agent Q-learning

Content Provider	IEEE Xplore Digital Library
Author	Abdoos, M. Mozayani, N. Bazzan, A.L.C.
Copyright Year	2011
Abstract	In many urban areas where traffic congestion does not have the peak pattern, conventional traffic signal timing methods does not result in an efficient control. One alternative is to let traffic signal controllers learn how to adjust the lights based on the traffic situation. However this creates a classical non-stationary environment since each controller is adapting to the changes caused by other controllers. In multi-agent learning this is likely to be inefficient and computationally challenging, i.e., the efficiency decreases with the increase in the number of agents (controllers). In this paper, we model a relatively large traffic network as a multi-agent system and use techniques from multi-agent reinforcement learning. In particular, Q-learning is employed, where the average queue length in approaching links is used to estimate states. A parametric representation of the action space has made the method extendable to different types of intersection. The simulation results demonstrate that the proposed Q-learning outperformed the fixed time method under different traffic demands.
Starting Page	1580
Ending Page	1585
File Size	225188
Page Count	6
File Format	PDF
ISBN	9781457721984
ISSN	21530009
e-ISBN	9781457721977
DOI	10.1109/ITSC.2011.6083114
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Vehicles Multiagent systems Delay Mathematical model Junctions
Content Type	Text
Resource Type	Article

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