Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network

no code implementations • 13 Feb 2021 • Takuma Oda

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.

reinforcement-learning Reinforcement Learning (RL)

MOVI: A Model-Free Approach to Dynamic Fleet Management

no code implementations • 13 Apr 2018 • Takuma Oda, Carlee Joe-Wong

Since DQNs scale poorly with a large number of possible dispatches, we streamline our DQN training and suppose that each individual vehicle independently learns its own optimal policy, ensuring scalability at the cost of less coordination between vehicles.

Management