Rebalancing Shared Mobility Systems by User Incentive Schemes: State-Action Representation Design and Analysis

In shared mobility systems research, there is increasing interest in the use of Reinforcement Learning (RL) techniques to both manage the resources and improve the service level (i.e., percentage of users served). The goal of using RL is to learn an effective data-driven user incentivization policy to encourage users of a shared mobility system (e.g. bikeshare systems) to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior are intended to over time increase the service level of the shared mobility system and improve its profitability. We present our study on the effects of state-action representation for a shared mobility system on performance for user incentivization, in terms of service level.