Multi-Agent Diagnostics for Robustness via Illuminated Diversity

arXiv:2401.13460v2 Announce Type: replace
Abstract: 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), …

abstract adversarial agent arxiv cs.ai cs.lg cs.ma diagnostics diversity environments multi-agent overfitting performance robustness systems training type via