Research on Adaptive Optimisation Model of Software System Based on Reinforcement Learning

With the continuous expansion of the scale of modern software systems and the rapid change of user requirements, system optimisation faces the dual challenges of dynamics and complexity. Traditional optimisation methods are difficult to meet the demands of current complex scenarios due to the lack of flexibility and real-time performance. To this end, this paper proposes an adaptive optimisation model for software systems based on reinforcement learning, which utilises the self-learning properties of reinforcement learning and the high-dimensional feature modelling capabilities of deep learning technology to design a set of solutions that can dynamically sense the changes in the environment and continuously optimise the system performance. The model solves the problem of multi-objective conflict in complex environments by constructing state space, action space and multi-objective reward mechanism, and achieves the dual goals of response time optimisation and resource utilisation enhancement. In addition, the model is experimentally verified in several dynamic scenarios in this paper, and the results show that the model significantly outperforms traditional methods in key indicators such as response time reduction rate and resource utilisation enhancement rate, which provides a new idea and practical support for the optimisation of software systems in complex dynamic environments.