AI model training method, AI model calling method, equipment and readable storage medium

Abstract

The invention discloses an AI model training method, an AI model calling method, equipment and a readable storage medium. The AI model training method comprises the following steps: acquiring metadatafor training, and loading a to-be-trained AI model, wherein the AI model is realized based on a deep reinforcement neural network; blocking the metadata of which the metadata belongs to the same frame to obtain a plurality of sub-metadata; performing feature extraction on the sub-metadata to obtain a feature vector corresponding to the sub-metadata; training the AI model according to the featurevector, and determining whether the trained AI model is converged or not; and if the trained AI model is not converged, executing a step that if the trained AI model is converged, the trained AI modelis sorted. The training sample of model training is improved, so that the training accuracy and training efficiency of the AI model are improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to an AI model training method, an AI model calling method, computer equipment, and a computer-readable storage medium. Background technique [0002] With the rapid development of artificial intelligence (AI) technology, artificial intelligence technology is widely used in various fields, for example, in the field of 3D modeling or 3D games, artificial intelligence technology can be used to better match characters Behavioral prediction and prediction, so that users can have a better experience in operation by giving more appropriate feedback. [0003] In the traditional game AI training method, one round corresponds to one process, and this method is often accompanied by a waste of machine resources. Especially for large-scale 3D and / or large-map games such as Chicken Eats, the machine resource overhead required for a game is usually very large. At th...

AI Technical Summary

Problems solved by technology

[0003] In the traditional game AI training method, one round corresponds to one process, and this method is often accompanied by a waste of machine resources

Especially for large-scale 3D and / or large-map games such as Chicken Eats, the machine resource overhead required for a game is usually very large

At the same time, it will also lengthen the entire decision-making process due to the larger map when predicting the model, which brings great challenges to reinforcement learning training.

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