Reinforcement Learning-Based Optimization and Simulation Modeling of International Relations Interaction Strategies

Analyzing the interaction strategies of international relations helps to understand and predict changes in the international landscape, so as to develop and optimize international interaction strategies.Firstly, a single-layer multi-temporal network is modeled for political events, scientific cooperation and international trade in international relations, and added to a multi-layer aggregation network.On this basis, a simulation and analysis method for simulating international relations interaction strategies based on deep learning and multi-intelligence body reinforcement learning methods is proposed.Applying the method of this paper to the arithmetic simulation analysis, it is found that the international relations in the last 10 years have shown the small-world characteristics, and cooperation and conflict coexist.Economic dependence is an influencing factor of conflict between two countries, when the economic and trade links are close, the two countries are less prone to conflict, so the optimization of the international relations interaction strategy should focus on the economic and trade relations.