Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control. (arXiv:2103.16223v3 [cs.LG] UPDATED)

Sub-optimal control policies in intersection traffic signal controllers (TSC)
contribute to congestion and lead to negative effects on human health and the
environment. Reinforcement learning (RL) for traffic signal control is a
promising approach to design better control policies and has attracted
considerable research interest in recent years. However, most work done in this
area used simplified simulation environments of traffic scenarios to train
RL-based TSC. To deploy RL in real-world traffic systems, the gap between
simplified simulation environments and …

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