Deep reinforcement learning air combat game interpretation method and system based on fuzzy decision tree

Abstract

The invention discloses a deep reinforcement learning air combat game interpretation method based on a fuzzy decision tree, and the method comprises the steps: carrying out the air combat game througha trained deep reinforcement learning model, and obtaining a training set and a feature set; constructing a membership function of each feature in the feature set, and fuzzifying the features one byone to obtain a fuzzy feature set after fuzzification of the feature set; establishing a fuzzy decision tree according to the training set and the fuzzy feature set; pruning the fuzzy decision tree byminimizing a loss function of the decision tree; traversing all paths of the pruned fuzzy decision tree, wherein each path represents an air combat game rule; storing the input and output of the deepreinforcement learning model during air combat game as to-be-processed data, and inputting the to-be-processed data into the pruned fuzzy decision tree to obtain a corresponding air combat game rule,thereby completing air combat game interpretation. According to the method, the problems of poor interpretability and non-intuitive result of a deep reinforcement learning algorithm are solved.

Description

technical field [0001] This application belongs to the technical field of air combat intelligent game and simulation deduction, and specifically relates to a deep reinforcement learning air combat game interpretation method and system based on fuzzy decision tree. Background technique [0002] Modern fighter jets are developing in the direction of high automation, informatization and intelligence. The battlefield environment is complex and changeable, and the information obtained by pilots is complicated and diverse. It is difficult to make the best planning and combat decisions in a short period of time only by the pilots themselves. [0003] Deep reinforcement learning is an artificial intelligence algorithm that does not rely on labeled samples. It learns knowledge through interaction with the environment, and improves the intelligence level of the decision-making system through continuous training and model iteration. Deep reinforcement learning is mainly oriented to seq...

AI Technical Summary

Problems solved by technology

[0005] The purpose of this application is to provide a deep reinforcement learning air combat game interpretation method and system ba...

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