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iTLM-Q: A Constraint-Based Q-Learning Approach for Intelligent Traffic Light Management

Roth, Christian ; Stöger, Lukas ; Nitschke, Mirja ; Hörmann, Matthias ; Kesdogan, Dogan



Abstract

Vehicle-to-everything (V2X) interconnects participants in vehicular environments to exchange information. This enables a broad range of new opportunities. For instance, crowdsourced information from vehicles can be used as input for self-learning systems. In this paper, we propose iTLM-Q based on our previous work iTLM to optimize traffic light management in a privacy-friendly manner. We aim to ...

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