PSN Game: Game-theoretic Prediction and Planning via a Player Selection Network

While game-theoretic planning frameworks are effective at modeling multi-agent interactions, they require solving large optimization problems where the number of variables increases with the number of agents, resulting in long computation times that limit their use in large-scale, real-time systems. To address this issue, we propose 1) PSN Game: a learning-based, game-theoretic prediction and planning framework that reduces runtime by learning a Player Selection Network (PSN); and 2) a Goal Inference Network (GIN) that makes it possible to use the PSN in incomplete information games where agents' intentions are unknown. A PSN outputs a player selection mask that distinguishes influential players from less relevant ones, enabling the ego player to solve a smaller, masked game involving only selected players. By reducing the number of players in the game, and therefore reducing the number of variables in the corresponding optimization problem, PSN directly lowers computation time. The PSN Game framework is more flexible than existing player selection methods as it 1) relies solely on observations of players' past trajectories, without requiring full state, action, or other game-specific information; and 2) requires no online parameter tuning. Experiments in both simulated scenarios and human trajectory datasets demonstrate that PSNs outperform baseline selection methods in 1) prediction accuracy; and 2) planning safety. PSNs also generalize effectively to real-world scenarios in which agents' objectives are unknown without fine-tuning. By selecting only the most relevant players for decision-making, PSN Game offers a general mechanism for reducing planning complexity that can be seamlessly integrated into existing multi-agent planning frameworks.