Abstract
Multi-robot coordination often exhibits hierarchical structure, with some robots’ decisions depending on the planned behaviors of others. While game theory provides a principled framework for such interactions, existing solvers struggle to handle mixed information structures that combine simultaneous (Nash) and hierarchical (Stackelberg) decision-making. We study N-robot forest-structured mixed-hierarchy games, introduce a quasi-policy approximation that removes higher-order policy derivatives, and develop an inexact Newton method for efficiently solving the resulting approximated KKT systems.
Type
Publication
Workshop on the Algorithmic Foundations of Robotics (WAFR) 2026
