Latency mitigation (or compensation) is one of the main concerns when developing online applications that rely on real-time interaction between users, as latency constraints for highly dynamic applications (e.g., competitive First-Person Shooters or racing games) are very strict. In a server-authoritative setting — i.e., a client-server configuration in which the server has the final say on any performed action — Artificial Intelligence (AI) enables a new solution for reducing the round-trip time of packets to and from users experiencing high latency. This thesis' contribution, in collaboration with the MPAI (Moving Picture, audio and data coding by Artificial Intelligence) Community, is an application of an Imitation Learning approach to a custom-made kart racing videogame, with the purpose of showing a possible implementation of the SPG (Server-based Predictive Multiplayer Gaming) specification for the steps that concern a single user (namely data gathering, model training and evaluation).