  In the rapidly advancing field of multi-agent systems, ensuring robustness in
unfamiliar and adversarial settings is crucial. Notwithstanding their
outstanding performance in familiar environments, these systems often falter in
new situations due to overfitting during the training phase. This is especially
pronounced in settings where both cooperative and competitive behaviours are
present, encapsulating a dual nature of overfitting and generalisation
challenges. To address this issue, we present Multi-Agent Diagnostics for
Robustness via Illuminated Diversity (MADRID), a novel approach for generating
diverse adversarial scenarios that expose strategic vulnerabilities in
pre-trained multi-agent policies. Leveraging the concepts from open-ended
learning, MADRID navigates the vast space of adversarial settings, employing a
target policy's regret to gauge the vulnerabilities of these settings. We
evaluate the effectiveness of MADRID on the 11vs11 version of Google Research
Football, one of the most complex environments for multi-agent reinforcement
learning. Specifically, we employ MADRID for generating a diverse array of
adversarial settings for TiZero, the state-of-the-art approach which "masters"
the game through 45 days of training on a large-scale distributed
infrastructure. We expose key shortcomings in TiZero's tactical
decision-making, underlining the crucial importance of rigorous evaluation in
multi-agent systems.
