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Emergency vehicles (EMVs) play a critical role in a city's response totime-critical events such as medical emergencies and fire outbreaks. Theexisting approaches to reduce EMV travel time employ route optimization andtraffic signal pre-emption without accounting for the coupling between routethese two subproblems. As a result, the planned route often becomes suboptimal.In addition, these approaches also do not focus on minimizing disruption to theoverall traffic flow. To address these issues, we introduce EMVLight in thispaper. This is a decentralized reinforcement learning (RL) framework forsimultaneous dynamic routing and traffic signal control. EMVLight extendsDijkstra's algorithm to efficiently update the optimal route for an EMV inreal-time as it travels through the traffic network. Consequently, thedecentralized RL agents learn network-level cooperative traffic signal phasestrategies that reduce EMV travel time and the average travel time of non-EMVsin the network. We have carried out comprehensive experiments with syntheticand real-world maps to demonstrate this benefit. Our results show that EMVLightoutperforms benchmark transportation engineering techniques as well as existingRL-based traffic signal control methods.

A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles