  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 real-world applications has to be
closed. 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. In addition to the realistic simulation model, LemgoRL encompasses a
traffic signal logic unit that ensures compliance with all regulatory and
safety requirements. LemgoRL offers the same interface as the wellknown OpenAI
gym toolkit to enable easy deployment in existing research work. To demonstrate
the functionality and applicability of LemgoRL, we train a state-of-the-art
Deep RL algorithm on a CPU cluster utilizing a framework for distributed and
parallel RL and compare its performance with other methods. Our benchmark tool
drives the development of RL algorithms towards real-world applications.
