  In case of a severe accident, the key to saving lives is the time between the
incident and when the victim receives treatment from the first-responders. In
areas with well designed emergency medical systems, the time for an ambulance
to arrive at the accident location is often not too long. However, in many low
and middle income countries, it usually takes much longer for an ambulance to
arrive at the accident location due to lack of proper services. On the other
hand, with ubiquitous wireless connectivity, and emergence of radio based
taxis, it seems feasible to build a low-cost emergency response system based on
taxi service. In this report, we explore one such solution for deployment of a
taxi-based emergency response systems using reinforcement learning.
