Vulnerability Analysis for Safe Multi-Agent Reinforcement

Key points

• Al- though existing SMARL methods focus on enforcing safety during policy optimization, their vulnerability remains largely unexplored.1
• This paper investigates inference-time observation attacks against SMARL, where an adversary perturbs the observations of a subset of agents, thereby altering the actions produced by their policies.1
• We propose MinMax Attack, an attack framework that jointly selects adversarial target actions for compromised agents while accounting for the Cooperative Resistance of benign agents.1
• Experiments across multiple safety-critical multi-agent benchmarks and representative state-of-the-art SMARL algorithms show that MinMax Attack induces substantially higher safety costs than existing attack baselines while preserving task reward, thereby reducing its detectability and achieving both attack effectiveness and stealthiness.1

Sources (1)

• [1]Vulnerability Analysis for Safe Multi-Agent Reinforcement
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 09:09 PM

  Safe Multi-Agent Reinforcement Learning (SMARL) aims to learn cooperative control policies under prescribed safety constraints.

  Al- though existing SMARL methods focus on enforcing safety during policy optimization, their vulnerability remains largely unexplored.

Extractive summary: sentences quoted from the sources.