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Traffic signal control (TSC) is a high-stakes domain that is growing inimportance as traffic volume grows globally. An increasing number of works areapplying reinforcement learning (RL) to TSC; RL can draw on an abundance oftraffic data to improve signalling efficiency. However, RL-based signalcontrollers have never been deployed. In this work, we provide the first reviewof challenges that must be addressed before RL can be deployed for TSC. Wefocus on four challenges involving (1) uncertainty in detection, (2)reliability of communications, (3) compliance and interpretability, and (4)heterogeneous road users. We show that the literature on RL-based TSC has madesome progress towards addressing each challenge. However, more work should takea systems thinking approach that considers the impacts of other pipelinecomponents on RL.

The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality