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Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network
[article]
2021
arXiv
pre-print
Ubiquitous mobile computing have enabled ride-hailing services to collect vast amounts of behavioral data of riders and drivers and optimize supply and demand matching in real time. While these mobility service providers have some degree of control over the market by assigning vehicles to requests, they need to deal with the uncertainty arising from self-interested driver behavior since workers are usually free to drive when they are not assigned tasks. In this work, we formulate the problem of
arXiv:2102.06854v1
fatcat:xy6ldr3rhbcrtgeocqhiwq2m5y