## 1 Introduction  
The creation of a recommendation system aims to solve the problem of increasing information explosion. The concept of _growth_ is key to the recommendation system. It mainly studies the characteristics of users and items, and better generates a recommendation list for users by mining the potential or explicit connection between users and items. The algorithms and models in the recommendation system train and simulate massive user and item feature data. This process often takes a lot of time and tries to improve the final recommendation accuracy because even a little improvement will generate huge profits in practical application. Therefore, the accuracy rate is so important in the recommendation system. It has become the first indicator to measure the algorithms and models of various recommendation systems. After decades of development, the recommendation system model has matured. From the early collaborative filtering algorithm [1] to the user-based recommendation algorithm [2] and then to the object-based recommendation algorithm [3], they have occupied the mainstream recommendation algorithm for a long period of time and have performed well until now. The proposal of matrix decomposition [4] provides a foundation for the development of factorization machines [5], and logistic regression [6], which is generated based on classification problems, has also become the mainstream algorithm of classification problem models. In the era of deep learning, the Wide and Deep model [7] combined _generalization_ and _memory_ for the first time, providing new ideas for subsequent models. Up to now, various models of recommendation systems have emerged endlessly, injecting new vitality into the recommendation system. Recommendation systems are tools for people to process information more conveniently, while reinforcement learning is fundamentally different in concept. It puts forward the concept of _agent_, and the core of reinforcement learning is exploration and learning. That is to say, reinforcement learning aims to make use of the agent to continuously interact with the environment, constantly try and update the exploration, and finally approach or even exceed the target. It can be said that the emergence of reinforcement learning has changed people's views on the prospect of artificial intelligence in a real sense, and countless researchers regard it as the real future of artificial intelligence. It is thanks to the continuous exploration and attempts of pioneers that reinforcement learning has continuously developed and evolved. There is a big gap between deep learning and reinforcement learning. Machine learning, the predecessor of deep learning, has matured after decades of continuous research and development and has made outstanding contributions in many aspects and fields. In recent years, the explosive development of reinforcement learning has won the attention of researchers, but its development is still in its initial stage, with great plasticity. With the integration of the two fields, the development of deep reinforcement learning is even more difficult. The perception ability of deep learning and the exploration ability of reinforcement learning make their application fields almost finalized and deeply accepted by people. A few models can jump out of the boundary of the two and learn from each other well. Based on this, the research in this paper tries to combine the mature and efficient deep learning model with the reinforcement learning algorithm with great potential to create a new high-performance model for the industry. Last year, we published the principle of the GAFM model [8], which we have studied for more than a year, and we have been working on the subsequent improvement of the model. The GAFM model solves the problem that the traditional recommendation system pays too much attention to accuracy, ignores running speed, and realizes the improvement of both accuracy and speed.