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

2026-05-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The Interaction-Breaking Adversarial Learning (IBAL) framework enhances robustness in cooperative multi-agent reinforcement learning (MARL) by addressing vulnerabilities in inter-agent coordination. Traditional robust MARL methods often focus on value-oriented attacks, neglecting disruptions to interaction structures. IBAL introduces an information-theoretic approach to construct attacks that impede coordination by perturbing agents' observations and actions. This framework partitions agents into two groups, $G_1$ and $G_2$, and uses mutual information (MI) to quantify cross-group influence. It then designs an observation attacker that masks informative observation dimensions and an action attacker that selects MI-minimizing actions. Empirical evaluations on the StarCraft II Multi-Agent Challenge (SMAC) demonstrate that IBAL consistently outperforms prior robust MARL baselines across diverse adversarial attacks and non-parametric perturbations, including scenarios where agents are missing or have reduced health.

Key takeaway

For research scientists developing robust multi-agent reinforcement learning systems, you should consider integrating interaction-breaking adversarial training. Focusing solely on value-oriented attacks leaves systems vulnerable to coordination failures. By explicitly modeling and attacking inter-agent influence using mutual information, your policies can learn to adapt to disrupted interactions, leading to significantly improved robustness and generalization across various adversarial and non-parametric perturbations.

Key insights

Disrupting inter-agent mutual information via observation and action attacks improves MARL robustness.

Principles

• Coordination fragility arises from interaction structure corruption.
• Mutual information quantifies cross-group influence.
• Training against interaction-breaking attacks enhances generalization.

Method

IBAL partitions agents into groups, quantifies cross-group influence using mutual information, and then applies observation masking and action perturbations to minimize this influence, training policies under these induced perturbed dynamics.

In practice

• Apply IBAL to QMIX for robust MARL.
• Randomly sample agent groups per episode for diverse attack exposure.
• Use CLUB for efficient dimension-wise MI estimation.

Topics

• Multi-Agent Reinforcement Learning
• Adversarial Learning
• Interaction-Breaking Attack
• Mutual Information
• Decentralized POMDP

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.