  We investigate the problem of autonomous racing among teams of cooperative
agents that are subject to realistic racing rules. Our work extends previous
research on hierarchical control in head-to-head autonomous racing by
considering a generalized version of the problem while maintaining the
two-level hierarchical control structure. A high-level tactical planner
constructs a discrete game that encodes the complex rules using simplified
dynamics to produce a sequence of target waypoints. The low-level path planner
uses these waypoints as a reference trajectory and computes high-resolution
control inputs by solving a simplified formulation of a racing game with a
simplified representation of the realistic racing rules. We explore two
approaches for the low-level path planner: training a multi-agent reinforcement
learning (MARL) policy and solving a linear-quadratic Nash game (LQNG)
approximation. We evaluate our controllers on simple and complex tracks against
three baselines: an end-to-end MARL controller, a MARL controller tracking a
fixed racing line, and an LQNG controller tracking a fixed racing line.
Quantitative results show our hierarchical methods outperform the baselines in
terms of race wins, overall team performance, and compliance with the rules.
Qualitatively, we observe the hierarchical controllers mimic actions performed
by expert human drivers such as coordinated overtaking, defending against
multiple opponents, and long-term planning for delayed advantages.
