Model training method, device, computer device and storage medium

Abstract

The present application relates to a model training method. The method includes acquiring a first sample set, wherein the first sample set includes a first sample image and behavior information of thefirst sample image, and the behavior information is used to indicate behavior action of a virtual object; training the model through the first sample set to obtain an initial model; acquiring a second sample set including a second sample image and behavior information of the second sample image, the second sample image being a scene picture image when the virtual object is controlled by an initial model; the initial model being retrained by the second sample set to obtain the object control model. It does not need long-time on-line training, nor need to prepare a large number of training samples, only a small number of training samples need to be prepared initially, and the subsequent samples in the training process of the initial model execution results are modified to obtain, so as to greatly improve the training efficiency of the virtual object control machine learning model in the virtual scene.

Description

technical field [0001] The embodiments of the present application relate to the technical field of machine learning, and in particular to a model training method, device, computer equipment, and storage medium. Background technique [0002] In many applications for building virtual scenes (such as virtual reality applications, 3D map programs, military simulation programs, first-person shooter games, multiplayer online tactical arena games, etc.), the system automatically controls the virtual objects in the virtual scene demand. [0003] In a related art, the automatic control of virtual objects in a virtual scene can be controlled by a well-trained deep reinforcement learning model. Among them, the deep reinforcement learning model is a machine learning model for online training. When training the deep reinforcement learning model, the developer defines the initial parameters for the deep reinforcement learning model in advance, and controls the virtual object online throu...

AI Technical Summary

Problems solved by technology

[0004] However, the deep reinforcement learning model in the related art needs a long time of online training, resulting in low efficiency of model training

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