Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning

Understand

While deep neural networks (DNNs) have strengthened the performance of cooperative multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by adversarial examples.

• Considering the safety critical applications of c-MARL, such as traffic management, power management and unmanned aerial vehicle control, it is crucial to test the robustness of c-MARL algorithm before it was deployed in reality.
• Existing adversarial attacks for MARL could be used for testing, but is limited to one robustness aspects (e.g., reward, state, action), while c-MARL model could be attacked from any aspect.
• To overcome the challenge, we propose MARLSafe, the first robustness testing framework for c-MARL algorithms.