Enhancing the Robustness of QMIX against State-adversarial Attacks
Weiran Guo Guanjun Liu Ziyuan Zhou Ling Wang Jiacun Wang
Deep reinforcement learning (DRL) performance is generally impacted by
state-adversarial attacks, a perturbation applied to an agent's observation.
Most recent research has concentrated on robust single-agent reinforcement
learning (SARL) algorithms against state-adversarial attacks. Still, there has
yet to be much work on robust multi-agent reinforcement learning. Using QMIX,
one of the popular cooperative multi-agent reinforcement algorithms, as an
example, we discuss four techniques to improve the robustness of SARL
algorithms and extend them to multi-agent scenarios. To increase the robustness
of multi-agent reinforcement learning (MARL) algorithms, we train models using
a variety of attacks in this research. We then test the models taught using the
other attacks by subjecting them to the corresponding attacks throughout the
training phase. In this way, we organize and summarize techniques for enhancing
robustness when used with MARL.