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Found 3 papers, 1 papers with code
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning
Subsequently, we evaluated the performance of the two methods on a separate model of the same intersection that was developed with a different traffic simulator.
Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking
We present an approach to ensure safety in a real-world intersection by using an action space that is safe by design.
Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control
Therefore, we propose LemgoRL, a benchmark tool to train RL agents as TSC in a realistic simulation environment of Lemgo, a medium-sized town in Germany.
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