🤖 AI Summary
This work addresses the challenge of Byzantine agents—those exhibiting faulty or malicious behavior—that undermine cooperative decision-making in multi-agent reinforcement learning (MARL) communication systems, a problem inadequately tackled by existing approaches. The paper proposes BARD-MARL, a novel post-hoc diagnostic framework that uniquely integrates policy graph trajectories with Bayesian trust modeling grounded in BayesG. By jointly analyzing inter-agent policy dependencies and communication reliability, BARD-MARL enables precise identification of Byzantine nodes. Evaluated in SUMO traffic signal control scenarios with 25 to 100 agents under diverse adversarial conditions—including observation flipping and coordinated attacks—the method achieves an AUC-ROC of up to 0.982, substantially outperforming baseline methods. These results demonstrate its effectiveness, scalability, and the critical advantage of decoupling coordination, detection, and mitigation mechanisms.
📝 Abstract
Learned communication improves coordination in cooperative multi-agent reinforcement learning, but it also creates a trust problem: a trained policy may route information through agents that have become faulty or adversarial. This paper studies Byzantine-agent detection for learned-communication MARL in adaptive traffic signal control. We propose BARD-MARL, a post-hoc diagnostic layer on top of BayesG, which is used as an attributed communication substrate rather than as a contribution of this paper. BARD-MARL combines two agent-level evidence streams: policy-graph features extracted from state-action trajectories and Bayesian trust statistics computed from BayesG latent mask probabilities. Across fixed-action, observation-flip, random-noise, and coordinated attacks in SUMO traffic grids, the results show that these signals are complementary rather than universally dominant. On a 25-agent grid, BARD-MARL reaches 0.843 AUC-ROC under a 10% observation-flip attack, while policy-graph-only detection reaches 0.917 AUC-ROC under a 10% coordinated attack. On a 100-agent grid, the unified BARD-MARL variant reaches 0.982 AUC-ROC for both 10% fixed-action and 10% coordinated attacks. The study shows that learned communication policies expose useful diagnostic evidence, but credible resilience claims require attack-specific ablations and explicit separation between coordination, detection, and mitigation.