Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning

Train teams to maintain cooperation when adversarial perturbations disrupt interactions between agents.

IBAL constructs observation and action perturbations that interfere with coordination. Adversarial training then teaches policies to remain effective under these interaction-focused disruptions, including evaluations with missing agents.