###### Abstract  
With the development of access technologies and artificial intelligence, a deep reinforcement learning (DRL) algorithm is proposed into channel accessing and anti-jamming. Assuming the jamming modes are sweeping, comb, dynamic and statistic, the DRL-based method through training can almost perfectly avoid jamming signal and communicate successfully. Instead, in this paper, from the perspective of jammers, we investigate the performance of a DRL-based anti-jamming method. First of all, we design an intelligent jamming method based on reinforcement learning to combat the DRL-based user. Then, we theoretically analyze the condition when the DRL-based anti-jamming algorithm cannot converge, and provide the proof. Finally, in order to investigate the performance of DRL-based method, various scenarios where users with different communicating modes combat jammers with different jamming modes are compared. As the simulation results show, the theoretical analysis is verified, and the proposed RL-based jamming can effectively restrict the performance of DRL-based anti-jamming method. deep reinforcement learning; Q-learning; intelligent anti-jamming; intelligent jamming Article On the Performance of Deep Reinforcement Learning-Based Anti-Jamming Method Confronting Intelligent Jammer  
[PERSON] 1,2,2,3,4,5,6,7,8,9,10,1  
proportionally [9]. When the power of jamming is low, the jamming could be invalid by choosing the appropriate communication power [6]. The anti-jamming strategies based on the principle of statistics [10], game theory [11], and the other approaches [12; 13] have excellent effects on reducing the probability of being jammed. In order to improve the jamming effect, anti-jamming methods are needed to take into consideration when we design a jamming method. There are many kinds of anti-jamming methods [14; 15; 16], such as frequency hopping [14], fixed frequency with a greater communication power [15], direct sequence spread spectrum (DSSS) [16]. In [17], link-layer jamming and the corresponding anti-jamming methods are overviewed. Link-layer jamming can analyze the user's Media Access Control (MAC) protocol parameters, and releases a high energy-efficiency jamming attack. In addition to the adversarial jamming, we can also utilize the cooperative jamming techniques to improve the physical-layer security [18]. In order to face the developing of anti-jamming, many researchers use optimization, game-theoretic or information theoretic principles to improve the jamming performance [19; 20; 21; 22]. However, these methods require some prior information (the communications protocol, user's transmission power, etc.) that is difficult to obtain in the actual environment. Other researchers solve the jamming problem with repeatedly interacting with the victim transmitter-receiver pair [23], combining optimization, game-theoretic theories and other theoretical tools. But these only take the situation that the user has a fixed strategy into consideration, or choose a strategy from a strategy set. It is difficult to design the efficient jamming strategy when facing an intelligent user that could change communication strategy by learning [24]. In [24], a dynamic spectrum access anti-jamming method based on a deep reinforcement learning (DRL) algorithm was designed. With a DRL algorithm, intelligent users can learn the jammer's strategy by trail-and-error channel accessing process and obtain the optimal anti-jamming policy with no need of jammer's information. As concluded in [24], even the space of spectral states is infinite, the powerful approximation capability of deep neural network can still converge to the optimal policy, and the success rate could get around 95% against sweeping, comb and dynamic jamming, which performs much better than the traditional reinforcement learning algorithm. The DRL-based anti-jamming method are powerful, but to the best of our knowledge, the efficient jamming approach against the DRL-based communication user has not been researched.