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In this article, we investigate intelligent anti-jamming communication method for wireless sensor networks. The stochastic game framework is introduced to model and analyze the multi-user antijamming problem, and a joint multi-agent anti-jamming algorithm (JMAA) is proposed to obtain the optimal anti-jamming strategy. In intelligent multi-channel blocking jamming environment, the proposed JMAA adopts multi-agent reinforcement learning to make online channel selection, which can effectively tackle the external malicious jamming and avoid the internal mutual interference among sensor nodes. The simulation results show that, the proposed JMAA is superior to the frequency-hopping method, the sensing-based method and the independent reinforcement learning. Specifically, the proposed JMAA has the higher average packet receive ratio than both the frequency-hopping method and the sensing-based method. Compared with the independent reinforcement learning, JMAA has faster convergence rate when reaching the same performance of average packet receive ratio. In addition, since the JMAA does not need to model the jamming patterns, it can be widely used for combating other malicious jamming such as sweep jamming and probabilistic jamming.INDEX TERMS Communication anti-jamming, channel selection, multi-agent reinforcement learning, Q-learning, wireless sensor networks.QUAN ZHOU was born in Liyang, China, in 1991. He received the B.S. degree in communication engineering from East China Jiaotong University, Nanchang, in 2014. He is currently pursuing the master's degree in electronics and communication engineering with the University of Army Engineering University of PLA, with a focus in intelligent communication anti-jamming method.

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