Design of an Intelligent Control Model for Enterprise Financial Risk Based on Deep Reinforcement Learning

This study proposes an intelligent control model for enterprise financial risk based on deep reinforcement learning, aiming to achieve effective prediction and control of enterprise financial risk through dynamic optimization strategy. The model design takes deep reinforcement learning as the core, constructs a multidimensional financial indicator system, and combines dual DQN and priority experience replay mechanism to improve training efficiency and model stability. By constructing state space, reward function and dynamic decision-making mechanism, the model can balance enterprise benefits and risks in a complex dynamic environment and achieve efficient intelligent decision-making. Experimental results show that the proposed model is superior to traditional methods in many performance indicators, has high decision-making accuracy and robustness, and provides innovative methods and theoretical support for enterprise financial risk management.