自動運転レースにおける一般化ナッシュ均衡の非正規化解
Non-Normalized Solutions of Generalized Nash Equilibrium in Autonomous Racing
共有制約付き動的ゲームにおいて、正規化を仮定しない一般化ナッシュ均衡を混合相補性問題として定式化し、レース場面での多様な相互作用を可能にする手法を提案した。
著者: Mark Pustilnik, Antonio Loquercio, Francesco Borrelli
分類: cs.RO, cs.GT, math.OC
原文アブストラクト
In dynamic games with shared constraints, Generalized Nash Equilibria (GNE) are often computed using the normalized solution concept, which assumes identical Lagrange multipliers for shared constraints across all players. While widely used, this approach excludes other potentially valuable GNE. This paper addresses the limitations of normalized solutions in racing scenarios through three key contributions. First, we highlight the shortcomings of normalized solutions with a simple racing example. Second, we propose a novel method based on the Mixed Complementarity Problem (MCP) formulation to compute non-normalized Generalized Nash Equilibria (GNE). Third, we demonstrate that our proposed method overcomes the limitations of normalized GNE solutions and enables richer multi-modal interactions in realistic racing scenarios.
関連論文
- 自律レースのための開ループ一般化ナッシュ均衡を解く逐次二次計画法ゲーム理論/自動運転
