  To better match drivers to riders in our ridesharing application, we revised
Lyft's core matching algorithm. We use a novel online reinforcement learning
approach that estimates the future earnings of drivers in real time and use
this information to find more efficient matches. This change was the first
documented implementation of a ridesharing matching algorithm that can learn
and improve in real time. We evaluated the new approach during weeks of
switchback experimentation in most Lyft markets, and estimated how it benefited
drivers, riders, and the platform. In particular, it enabled our drivers to
serve millions of additional riders each year, leading to more than $30 million
per year in incremental revenue. Lyft rolled out the algorithm globally in
2021.
