Fault Tolerant Multi-agent Learning With Adversarial Budget Constraints

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, a

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keymguni2025fault

Related papers

• Byzantine Robust Cooperative Multi-agent Reinforcement Learning As A Bayesian Game (2023)0.00
• Robust Multi-agent Reinforcement Learning With State Uncertainty (2023)0.00
• Breaking The Curse Of Multiagency In Robust Multi-agent Reinforcement Learning (2024)0.00
• Collaborative Adaptation For Recovery From Unforeseen Malfunctions In Discrete And Continuous MARL Domains (2024)3.58
• Attacking C-marl More Effectively: A Data Driven Approach (2022)0.00
• Adversarial Attacks In Consensus-based Multi-agent Reinforcement Learning (2021)0.00
• Risk-aware Distributed Multi-agent Reinforcement Learning (2023)3.58
• Attention-based Fault-tolerant Approach For Multi-agent Reinforcement Learning Systems (2019)0.00