Action learning provides a unified, reusable objective for reasoning about dynamical systems from trajectory data—enabling path ranking, control, and inference without retraining for each new task.
This paper introduces LEAP, a method for learning stochastic actions from trajectory observations in dynamical systems. By anchoring unknown actions to a reference process and learning corrections through trajectory comparison, LEAP creates a differentiable objective that captures both local dynamics and long-term dependencies.