前方・逆動的ゲーム統合フレームワークによるマルチエージェント軌道計画の高度化
A Forward-Inverse Dynamic Game Framework for Enhanced Multi-Agent Trajectory Planning
未知の目的関数を持つ非線形システムにおけるマルチエージェント軌道計画を、KL正則化と状態依存重みを導入した動的ゲームと、最大エントロピー逆強化学習による逆ゲームモジュールを組み合わせて解く手法を提案した。
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著者: Tianle Liu, Youcheng Niu, Jing Zeng, Shuo Li, Jinming Xu
分類: cs.RO
原文アブストラクト
This paper studies feedback Nash equilibrium (FBNE) seeking for multi-agent trajectory planning in nonlinear dynamical systems with unknown agents' objectives and state-dependent inter-agent coupling. While dynamic game theory provides a principled framework for such problems, existing approaches typically assume fully rational agents with known objectives or rely on fixed regularization, limiting their ability to capture bounded rationality and spatially varying interaction intensity in safety-critical settings. To this end, we propose a KL-regularized dynamic game with a state-dependent weight that adaptively balances optimality and behavioral priors. To infer unknown cost parameters from demonstrated behaviors, we develop a context-aware inverse game module based on maximum-entropy inverse reinforcement learning with physics-informed regularization, ensuring structural consistency with the forward game. We establish per-iteration well-posedness of the regularized local game and show that the adaptive weighting function remains Lipschitz continuous under bounded nominal-trajectory updates. Numerical simulations and multi-robot experiments on cooperative navigation and merging scenarios validate the effectiveness of the proposed framework.
