Resilient Multi-Agent Reinforcement Learning for Tiered Mixed Autonomy

Tiered Mixed Autonomy (TMA) represents a transformative transportation paradigm where autonomous vehicles (AVs) with varying intelligence levels interact dynamically with human-driven vehicles (HVs) under asymmetric sensing, communication constraints, and task objectives. Unlike conventional autonomy systems, TMA exhibits multidimensional heterogeneity across autonomy tiers, introducing unprecedented challenges in coordination and resilience. However, existing decision-making frameworks fail to resolve coordination complexity and systemic fragility in TMA, particularly in handling noise-induced vulnerability within partially observable environments. Here we propose a novel resilient cluster-based decision-making framework for asymmetrical noisy TMA. First, a Cluster-based Noisy Partially Observable Markov Decision Process (CNMDP) formally characterizes multilayered interactions and asymmetrical observation uncertainties among heterogeneous agents. Additionally, a cluster-graph representation models intra-cluster spatiotemporal dynamics and resolves hierarchical inter-cluster dependencies. Finally, the Resilient Q-Nexus Engine (RQNE) enhances decision robustness via a noise-aware weighting mechanism and a Huber loss function, ensuring stable convergence under dynamic disturbances. Experimental results demonstrate comprehensive performance advantages and superior resilience. Notably, under 12% noise variance, the framework exhibits only 4.34% performance degradation while maintaining 91.486% inter-cluster coordination efficiency. These findings pave the way for deploying resilient TMA systems in real-world dynamic traffic networks, encompassing urban street grids and highway corridors with merging lanes, on-ramps, off-ramps and varying traffic densities.