Budget-Efficient Attacks and Robustness Training for Cooperative MARL

Cooperative multi-agent reinforcement learning (CMARL) policies are vulnerable to action hijacking even when only a few timesteps are compromised. Recent adversarial attacks and adversarial training methods have been explored, but under an explicit attack budget, existing attacks often fail to accurately expose critical coordination weaknesses and incur substantial training cost. We propose Budgeted Hierarchical Efficient Attack (BHEA), a budgeted hierarchical adversarial attack that separates decisions on when and which agents to hijack from action replacement, enabling more precise vulnerability discovery under limited attack opportunities. We further show that training cooperative policies against BHEA substantially improves robustness to limited-step action hijacking while reducing training overhead. Experiments on the Star-Craft Multi-Agent Challenge (SMAC) and Multi-Agent Particle Environments (MPE) demonstrate stronger attacks under the same attack budget and improved robustness. Code is available at https://github.com/ji6ng/BHEA.