User behavior prediction and interface adaptive algorithm design based on agent reinforcement learning in mobile application development

The reinforcement learning (RL) technology of agent is introduced, and through the interactive learning between agent and environment, the strategy is optimized to maximize the cumulative reward, thus realizing the accurate prediction of user behavior and the dynamic adjustment of interface layout. In the algorithm design, the state space is composed of user behavior data and interface layout data, the action space covers all possible interface adjustment operations, and the reward function is evaluated by combining user satisfaction and interface layout rationality. Based on the Deep Q-Network (DQN) algorithm, this paper improves the model architecture, adopts the dual-network architecture and the mechanism of preferential experience playback, and introduces dual encoders (LSTM and CNN) to encode user behavior and interface layout respectively, so as to enhance the feature extraction ability. In the experimental part, R6D data set and self-built data set are used for verification. The results show that the proposed DRL-UI scheme is superior to the traditional methods in key indicators such as prediction accuracy, interface layout score, response delay and memory occupation. The ablation experiment further proved the contribution of each component to the performance improvement. The complete model DRL-UI achieved the best performance, and the CTR and user stay time increased by 31.5% and 73.2 seconds respectively. In addition, the real-time test shows that the time consumption of layout calculation, strategy reasoning and rendering update is 28 milliseconds, 19 milliseconds and 15 milliseconds respectively, indicating that optimizing the time of layout calculation and strategy reasoning is the key to further reduce the response delay.