minimize the total energy consumption of straight-line data transmission from node to base station and obtained the ideal path with the minimum energy consumption. However, the throughput of the destination node was not considered; only the energy consumption was considered. In Reference [11], an MDCE routing algorithm was proposed for a delay-tolerant network, which solves the problem of the high load rate and packet loss rate in delay-tolerant networks. However, this algorithm only considers the local topology of the network, not the global topology. Reference [12] proposed a multi-channel anti-jamming routing protocol algorithm based on LEACH, which can switch the channel in a timely manner according to the interference and effectively avoid malicious jamming. However, this algorithm is only applicable to a network with low density. In [13], the aim was to use the spectral-efficient IBFD scheme to improve the security of the system with minimum interference. Interference can be reduced by the addition of orthogonality between the transmitted and received signals of a relay. However, the proposed algorithm does not consider external interference. In Reference [14], an Internet-of-Things (IoT) system containing a relay selection was studied by employing an emerging multiple access scheme, namely, non-orthogonal multiple access (NOMA). However, the algorithm proposed here considers wiretap jamming. Reference [15] proposed an anti-jamming path selection method based on the MAB model under full-frequency blocking jamming, but the proposed algorithm needs to exhaust all available paths, resulting in high computational complexity. To sum up, the shortcomings of the existing literature are that there are few studies considering electromagnetic jamming, and existing routing anti-jamming methods [16; 17; 18] have problems of high complexity, poor convergence effect, and poor scalability.  
The anti-jamming decision of intelligent communication is usually modeled as a Markov decision process (MDP) [19]. In practical application scenarios, it is difficult to obtain the state transition probability function [20], so reinforcement learning can be adopted. Reinforcement learning takes environmental feedback as input, constantly interacts with the environment, and learns the optimal action strategy through trial and error. Because of the complex time variation of the electromagnetic environment, model-free reinforcement learning is a common method for intelligent anti-jamming communication. However, an algorithm based on model-free reinforcement learning has the problems of \"dimensional disaster\" and poor convergence. The motivation is that the transmission distance of a single communication node is limited, and other algorithms have difficulty quickly finding the optimal path. Therefore, we designed the proposed scheme. In this study, the model-based and model-free [21] methods were applied to multi-hop path anti-jamming, and a model-based reinforcement learning (Dyna-Q) anti-jamming path selection algorithm framework was designed [22]. The algorithm proposed in this paper is aimed at a multi-node wireless communication network. When data are sent from the source to the host [23] in an environment of broadband blocking jamming, the Dyna-Q algorithm is adopted to select relay nodes, thus forming the optimal communication path. The main contribution of this paper is as follows: Aiming at the problem of path selection in a broadband jamming environment, an anti-jamming algorithm based on Dyna-Q is proposed for wireless communication networks.