关键词: 无人博弈对抗, 复杂系统建模, 智能决策, 深度强化学习, 策略演进

Abstract:

In view of the complex situation of the current game which will be large-scale, high-intensity, not omniscient, and strong confrontation, and in response to the lack of flexibility and long iteration cycles in traditional game decision-making, the model of the unmanned complex game system is built according to the background of the unmanned red and blue game. Based on deep reinforcement learning technology, intelligent decision-making algorithms are studied in the background of unmanned red and blue games. With the help of deep neural networks and Bellman's optimal principle, the search of the huge solution space is more efficient, and the optimal intelligent decision is constructed in complex game scenes. The network structure and training algorithm of the decision-making agent are designed in order to achieve optimal game efficiency, strategy evolution as well as rapid iteration. And the effectiveness, flexibility and generalization ability of the proposed algorithm are verified through simulation.

Key words: unmanned game confrontation, complex system modeling, intelligent decision-making, deep reinforcement learning, strategic evolution