2010
DOI: 10.1007/978-3-642-11688-9_18
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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 throughput and reduced 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 solu… Show more

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Cited by 67 publications
(34 citation statements)
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References 19 publications
(26 reference statements)
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“…Although simple, similar networks have been used in other tra c experiments [3,4]. A single Grid City block measures 200 meters.…”
Section: Methodsmentioning
confidence: 99%
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“…Although simple, similar networks have been used in other tra c experiments [3,4]. A single Grid City block measures 200 meters.…”
Section: Methodsmentioning
confidence: 99%
“…Tra c lights are probably the most prevalent means of controlling tra c. Other methods include stop signs and roundabouts. Although many tra c lights rely on simple fixed protocols, they are none-the-less a vital component of tra c management [4]. More advanced adaptive Urban Tra c Controllers (UTC), such as RHODES [17], OPAC [11] and SCOOT 1 , have been developed in an e↵ort to improve the performance of tra c lights [22,18].…”
Section: Introductionmentioning
confidence: 99%
“…Step 4:Agent calculate reward value t r based on equation (5)and update learning rate t a based on equation (6).…”
Section: A Collaborative Q-learning Algorithemmentioning
confidence: 99%
“…Since traffic control is fundamentally a problem of sequential decision making, and at the same time is a task that is too complex for straightforward computation of optimal solutions or effective hand-coded solutions, it is perhaps best suited to the framework ofMarkov Decision Processes (MDPs) and reinforcement learning (RL) or approximate dynamic programming (ADP), in which an agent learns from trial and error via interaction with its environment [5].…”
Section: Introductionmentioning
confidence: 99%
“…Poorly designed phase plans may lead to additional delays, traffic jams and even accidents. Although many traffic signals rely on simple fixed protocols, they are nonetheless a vital component of traffic management [1]. Historically, finding the best signal timings involved using mathematical models of traffic behaviour to determine ideal settings [22].…”
Section: Introductionmentioning
confidence: 99%