A Better Match for Drivers and Riders Reinforcement Learning at Lyft

The paper demonstrates the successful application of reinforcement learning to improve the efficiency of driver-rider matching in ride-sharing platforms. The use of online RL allows for real-time adaptation, resulting in decreased wait times for riders, increased earnings for drivers, and overall higher user satisfaction. The research paves the way for more intelligent systems in the ride-sharing industry, with potential for further optimization and expansion into various other aspects of the ecosystem.

Read full paper: https://arxiv.org/abs/2310.13810

Tags: Reinforcement Learning, Recommender Systems, Machine Learning