IBAL framework constructs information-theoretic adversarial attacks on agent observations and actions to train MARL agents that remain robust to interaction disruptions and agent-missing scenarios.
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Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
IBAL framework constructs information-theoretic adversarial attacks on agent observations and actions to train MARL agents that remain robust to interaction disruptions and agent-missing scenarios.