The IBAL framework builds information-theoretic attacks that break agent interactions in MARL and trains policies to stay robust under observation and action perturbations.
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Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
The IBAL framework builds information-theoretic attacks that break agent interactions in MARL and trains policies to stay robust under observation and action perturbations.