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The growth of autonomous vehicles, ridesharing systems, and self drivingtechnology will bring a shift in the way ride hailing platforms plan out theirservices. However, these advances in technology coupled with road congestion,environmental concerns, fuel usage, vehicles emissions, and the high cost ofthe vehicle usage have brought more attention to better utilize the use ofvehicles and their capacities. In this paper, we propose a novel multi-hopride-sharing (MHRS) algorithm that uses deep reinforcement learning to learnoptimal vehicle dispatch and matching decisions by interacting with theexternal environment. By allowing customers to transfer between vehicles, i.e.,ride with one vehicle for sometime and then transfer to another one, MHRS helpsin attaining 30\% lower cost and 20\% more efficient utilization of fleets, ascompared to the ride-sharing algorithms. This flexibility of multi-hop featuregives a seamless experience to customers and ride-sharing companies, and thusimproves ride-sharing services.

A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning