Title: Robust Multi-Agent Reinforcement Learning against Adversaries on Observation. Abstract: With the broad applications of deep learning, such as image classification, it is becoming increasingly essential to tackle the vulnerability of neural networks when facing adversarial attacks, which have been widely studied recently. In the cooperative multi-agent reinforcement learning field, which has also shown potential in real-life domains, little work focuses on the problem of adversarial attacks. However, adversarial attacks on observations that can undermine the coordination among agents are likely to occur in actual deployment. This paper proposes a training framework that progressively generates adversarial attacks on agents' observations to help agents learn a robust cooperative policy. One attacker makes decisions on a hybrid action space that it first chooses an agent to attack and then outputs the perturbation vector. The victim policy is then trained against the attackers. Experimental results show that our generated adversarial attacks are diverse enough to improve the agents' robustness against possible disturbances. 