Fault Tolerant Multi-Agent Learning with Adversarial Budget Constraints

David Mguni; Yaqi Sun; Haojun Chen; Wanrong Yang; Amir Darabi; Larry Olanrewaju Orimoloye; Yaodong Yang

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arXiv · 2508.08800

Fault Tolerant Multi-Agent Learning with Adversarial Budget Constraints

David Mguni·Yaqi Sun·Haojun Chen·Wanrong Yang·Amir Darabi·Larry Olanrewaju Orimoloye·Yaodong Yang

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Abstract

We study robustness to agent malfunctions in cooperative multi-agent reinforcement learning (MARL), a failure mode that is critical in practice yet underexplored in existing theory. We introduce MARTA, a plug-and-play robustness layer that augments standard MARL algorithms with a Switcher-Adversary mechanism which selectively induces malfunctions in performance-critical states. This formulation defines a fault-switching $(N+2)$-player Markov game in which the Switcher chooses when and which agent fails, and the Adversary controls the resulting faulty behaviour via random or worst-case policies. We develop a Q-learning-type scheme and show that the associated Bellman operator is a contraction, yielding existence and uniqueness of the minimax value, convergence to a Markov perfect equilibrium. MARTA integrates seamlessly with MARL algorithms without architectural modification and consistently improves robustness across Traffic Junction (TJ), Level-Based Foraging (LBF), MPE SimpleTag, and SMAC (v2). In these domains, MARTA achieves large gains in final performance of up to 116.7\% in SMAC, 21.4\% in MPE SimpleTag, and 44.6\% in LBF, while significantly reducing failure rates under train-test mismatched fault regimes. These results establish MARTA as a theoretically grounded and practically deployable mechanism for fault-tolerant MARL.

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BibTeX RIS

David Mguni, Yaqi Sun, Haojun Chen, Wanrong Yang, Amir Darabi, Larry Olanrewaju Orimoloye, Yaodong Yang. 2026-02-19. Fault Tolerant Multi-Agent Learning with Adversarial Budget Constraints. https://arxiv.org/abs/2508.08800

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