How can I make my MARL agents robust to coordinated attacks?
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
February 6, 2025
https://arxiv.org/pdf/2502.02844This paper introduces the "Wolfpack Attack," a new method for training more robust multi-agent AI systems. It simulates coordinated attacks on multiple agents, inspired by how wolves hunt in packs. This exposes vulnerabilities in existing training methods that typically only defend against single-agent attacks. To counter this new attack, they also introduce the Wolfpack-Adversarial Learning (WALL) training framework, which improves the agents' ability to cooperate and defend as a team.
Key points relevant to LLM-based multi-agent systems:
- Coordinated Adversarial Training: The Wolfpack attack highlights the importance of considering coordinated adversarial actions when training multi-agent LLM systems, pushing for more robust and realistic training scenarios.
- Enhanced Collaboration: WALL's focus on system-wide collaboration could inspire new techniques for training LLMs to cooperate more effectively in multi-agent settings. This is particularly relevant for applications where emergent group behavior is desired.
- Robustness against Diverse Attacks: While designed for a specific attack, WALL's success suggests that training against diverse and complex attacks is crucial for robust multi-agent LLM development. This could involve combining different adversarial strategies during training.
- Planner-Based Attacking: The use of a planner to select critical attack timings underscores the potential of using planning algorithms in conjunction with LLMs to generate more strategic and impactful actions in multi-agent scenarios. This could improve the efficiency and effectiveness of adversarial training.