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 op-erational decision-making experience is difficult to express and the training samples for intelligent decision learning are limit-ed,based on the joint operational simulation and drill environment,a decision-making method based on generative adversari-al imitation learning is proposed.This method integrates the operational decision-making experience representation and learn-ing 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 in-telligent agents in scenarios.This method achieved expected results in the constructed typical adversarial scenarios.The algo-rithm training converged and the decisions output by the intelligent agent are reasonable.Preliminary experimental results in-dicate that generative adversarial imitation learning,as an intelligent operational decision-making method,has value for fur-ther research.
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