Learning to Play Trajectory Games Against Opponents with Unknown Objectives

Understand

Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another.

• Game theory provides a natural mathematical tool for robot motion planning in such interactive settings.
• However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in the scene are known.
• To make such tools applicable for ego-centric planning with only local information, we propose an adaptive model-predictive game solver, which jointly infers other players' objectives online and computes a corresponding generalized Nash equilibrium (GNE) strategy.