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.

本研究提出了 DATI，一个使用循环一致的生成对抗方法设计的迁移轨迹模仿的深度强化学习代理，在多个合成轨迹模式的实验中表现优于基线模型和最优控制，可以用于异常运动识别和空间不受限的轨迹数据挖掘。