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

In three linesNew IBAL method to strengthen MARL robustness against inter-agent interaction disruptions. Framework uses information-theoretic approach to construct attacks that degrade coordination by perturbing observations and actions, then trains agents to remain reliable. Demonstrated improvement over existing baselines and agent-missing scenarios.

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