## 1 Introduction
In [6], a deep reinforcement learning-based maritime anti-jamming algorithm was proposed to address the issue of wireless communication interruptions in a maritime communications environment due to tracking jamming. The algorithm reduced the probability of communication systems being found and tracked by tracking jammers and lowered the bit error rate of maritime wireless transmission, and the proposed algorithm also helped save the energy consumption of the ship and drone platforms. As can be seen from the existing literature, deep reinforcement learning techniques can help wireless communication systems learn from their environment and adjust their strategies according to the jamming and jamming they encounter in various application scenarios, thus making optimal anti-jamming decisions to ensure their performance of signal transmissions [7; 8; 9; 10; 11; 12; 13; 14; 15]. However, existing deep reinforcement learning anti-jamming algorithms still face two challenges. First, their robustness is expected to be further improved. When faced with the same jamming pattern, even a minor adjustment to jamming parameters can compromise or even fail their convergence strategy, causing the algorithms to start their relearning process, which can be very time-consuming. Secondly, the proposed algorithms still lack generalization ability, and their applications are still limited in combating some specific jamming patterns. Most of the existing algorithms still lack the ability to explore their previous anti-jamming task experiences to accelerate their learning process for implementing new anti-jamming tasks. Meta-learning is a branch of General AI, which aims to enable machines to learn like humans, possessing cognitive and logical analytical abilities, and to be able to realize a self-update so as to adapt to new learning tasks. In 2017, [PERSON] from the Bair lab proposed the Model Agnostic Meta-Learning (MAML) algorithm for the first time in [16]. This algorithm can quickly and accurately transfer a well-trained deep learning model to a new environment. Unlike transfer learning, MAML-based algorithms can complete their decision-making tasks using a meta-learner (meta-layer) to guide their learner (base layer) to reach the optimized decisions. The meta-layer can let the algorithms combine their previous learning experiences from all of their past tasks, providing the necessary initial model values for their learning process for new tasks. MAML is applicable to all optimized learners based on stochastic gradient descent. Hence, it is possible to integrate an MAML module into all deep learning models, which can improve their generalization ability and greatly enlarge their application scenarios while maintaining the algorithm performance [17]. In [18], the author addressed the issue that deep neural networks usually perform well in data-rich situations but poorly when data are less available or when rapid adaptation is needed for task changes. They introduced a meta-learner called SNAIL (Simple Neural Attentive Learner), which offered an effective solution for deep neural networks in data-scarce environments or when rapid task adaptation is needed and excellent performance was achieved in some multiple-task scenarios. In [19], a facial recognition algorithm (Meta Face Recognition (MFR) Algorithm) was proposed by using the meta-learning technique. This algorithm addressed the poor generalization ability of the facial recognition systems used in real-life applications and can effectively apply models well-trained with webface data to real-life surveillance scenarios to improve their performances. The existing works show that MAML-based algorithms require fewer training samples and have better generalization abilities and they can especially enable intelligent anti-jamming decisions to fully exploit their experiences from previous anti-jamming tasks to make optimal decisions in a faster manner when faced with jamming patterns with a few parameter changes [20; 21; 22; 23; 24; 25].