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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Demonstration of Multiagent Reinforcement Learning Applied to Traffic Light Signal Control
verfasst von : Carolina Higuera, Fernando Lozano, Edgar Camilo Camacho, Carlos Hernando Higuera
Erschienen in: Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection
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Abstract
We present a demonstration of two coordination methods for the application of multiagent reinforcement learning to the problem of traffic light signal control to decrease travel time. The first approach that we tested exploits the fact that the reward function can be splitted into contributions per agent. The second method computes the best response for a two player game with each member of its neighborhood. We apply both learning methods through SUMO traffic simulator, using data from the Transit Department of Bogotá, Colombia.