A decision-making method based on generative adversarial imitation learning

To study the intelligent decision making methods under limited decision samples, aiming at the problems that operational decision-making experience is difficult to express and the training samples for intelligent decision learning are limited, based on the joint operational simulation and drill environment, a decision-making method based on generative adversarial imitation learning is proposed. This method integrates the operational decision-making experience representation and learning process. On the basis of high-level decision-making and low-level action, rule definitions are used to specify the logic of task execution, and generative adversarial imitation learning algorithms are utilized to improve the generalization ability of intelligent agents in scenarios. This method achieved expected results in the constructed typical adversarial scenarios. The algorithm training converged and the decisions output by the intelligent agent are reasonable. Preliminary experimental results indicate that generative adversarial imitation learning, as an intelligent operational decision-making method, has value for further research.