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<p>Created page with &quot; == Abstract == Traffic light control is one of the main means of controlling road traffic. Improving traffic control is important because it can lead to higher traffic throu...&quot;</p> <p><b>New page</b></p><div><br /> == Abstract ==<br /> <br /> Traffic light control is one of the main means of controlling road traffic. Improving traffic control is important because it can lead to higher traffic throughput and reduced traffic congestion. This chapter describes multiagent reinforcement learning techniques for automatic optimization of traffic light controllers. Such techniques are attractive because they can automatically discover efficient control strategies for complex tasks, such as traffic control, for which it is hard or impossible to compute optimal solutions directly and hard to develop hand-coded solutions. First, the general multi-agent reinforcement learning framework is described, which is used to control traffic lights in this work. In this framework, multiple local controllers (agents) are each responsible for the optimization of traffic lights around a single traffic junction, making use of locally perceived traffic state information (sensed cars on the road), a learned probabilistic model of car behavior, and a learned value function which indicates how traffic light decisions affect longterm utility, in terms of the average waiting time of cars. Next, three extensions are described which improve upon the basic framework in various ways: agents (traffic junction controllers) taking into account congestion information from neighboring agents; handling partial observability of traffic states; and coordinating the behavior of multiple agents by coordination graphs and the max-plus algorithm. © 2010 Springer-Verlag Berlin Heidelberg.<br /> <br /> <br /> == Original document ==<br /> <br /> The different versions of the original document can be found in:<br /> <br /> * [http://dx.doi.org/10.1007/978-3-642-11688-9_18 http://dx.doi.org/10.1007/978-3-642-11688-9_18]<br /> <br /> * [http://resolver.tudelft.nl/uuid:a08335ed-3c0d-4cb6-98d0-97e88e7c7008 http://resolver.tudelft.nl/uuid:a08335ed-3c0d-4cb6-98d0-97e88e7c7008]<br /> <br /> * [http://www.cs.ox.ac.uk/people/shimon.whiteson/pubs/bakkericis10.pdf http://www.cs.ox.ac.uk/people/shimon.whiteson/pubs/bakkericis10.pdf],<br /> : [https://link.springer.com/chapter/10.1007%2F978-3-642-11688-9_18 https://link.springer.com/chapter/10.1007%2F978-3-642-11688-9_18],<br /> : [https://core.ac.uk/display/85537339 https://core.ac.uk/display/85537339],<br /> : [https://repository.tudelft.nl/view/tno/uuid:a08335ed-3c0d-4cb6-98d0-97e88e7c7008 https://repository.tudelft.nl/view/tno/uuid:a08335ed-3c0d-4cb6-98d0-97e88e7c7008],<br /> : [https://www.narcis.nl/publication/RecordID/oai%3Adare.uva.nl%3Apublications%2F1d92c43b-5004-4aa9-9487-1c617f5d6eba https://www.narcis.nl/publication/RecordID/oai%3Adare.uva.nl%3Apublications%2F1d92c43b-5004-4aa9-9487-1c617f5d6eba],<br /> : [https://www.cs.ox.ac.uk/publications/publication10161-abstract.html https://www.cs.ox.ac.uk/publications/publication10161-abstract.html],<br /> : [https://dx.doi.org/10.1007/978-3-642-11688-9_18 https://dx.doi.org/10.1007/978-3-642-11688-9_18],<br /> : [http://dx.doi.org/10.1007/978-3-642-11688-9_18 http://dx.doi.org/10.1007/978-3-642-11688-9_18],<br /> : [https://rd.springer.com/chapter/10.1007/978-3-642-11688-9_18 https://rd.springer.com/chapter/10.1007/978-3-642-11688-9_18],<br /> : [https://dblp.uni-trier.de/db/series/sci/sci281.html#BakkerWKG10 https://dblp.uni-trier.de/db/series/sci/sci281.html#BakkerWKG10],<br /> : [https://academic.microsoft.com/#/detail/2162115371 https://academic.microsoft.com/#/detail/2162115371]<br /> <br /> * [http://www.springerlink.com/index/pdf/10.1007/978-3-642-11688-9_18 http://www.springerlink.com/index/pdf/10.1007/978-3-642-11688-9_18],<br /> : [http://dx.doi.org/10.1007/978-3-642-11688-9_18 http://dx.doi.org/10.1007/978-3-642-11688-9_18]</div>

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