  Domain-adaptive trajectory imitation is a skill that some predators learn for
survival, by mapping dynamic information from one domain (their speed and
steering direction) to a different domain (current position of the moving
prey). An intelligent agent with this skill could be exploited for a diversity
of tasks, including the recognition of abnormal motion in traffic once it has
learned to imitate representative trajectories. Towards this direction, we
propose DATI, a deep reinforcement learning agent designed for domain-adaptive
trajectory imitation using a cycle-consistent generative adversarial method.
Our experiments on a variety of synthetic families of reference trajectories
show that DATI outperforms baseline methods for imitation learning and optimal
control in this setting, keeping the same per-task hyperparameters. Its
generalization to a real-world scenario is shown through the discovery of
abnormal motion patterns in maritime traffic, opening the door for the use of
deep reinforcement learning methods for spatially-unconstrained trajectory data
mining.
