Method for training neural network based on genetic algorithm to control game role behaviors

A neural network control and game character technology, which is applied in the field of neural network control game character behavior based on genetic algorithm training, can solve the problems of decreased interest of players and influence of word of mouth of game companies, etc.

Inactive Publication Date: 2019-08-02
ZHEJIANG UNIV OF TECH
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  • Abstract
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AI Technical Summary

Problems solved by technology

The advantage lies in stability, and the disadvantages are also obvious. The behavior is predictable, and there will be situations that human beings cannot consi

Method used

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  • Method for training neural network based on genetic algorithm to control game role behaviors
  • Method for training neural network based on genetic algorithm to control game role behaviors
  • Method for training neural network based on genetic algorithm to control game role behaviors

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Embodiment Construction

[0058] The present invention will be described in detail below according to the accompanying drawings and preferred embodiments, and the purpose and effect of the present invention will become clearer. The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0059] This example chooses to use the Unity3D game engine to provide a training environment, such as figure 1 As shown, the method is divided into the following steps:

[0060] (1) Group initialization

[0061] The initial value I of the number of groups is set to 80, which is recorded as group P, the training generation time T is set to 60s, and the time for each frame of the computer is T 0 ,T 0 Be a constant, g=0, t=0, the hereditary algebra G of planned training is 120 generations;

[0062] (2...

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Abstract

The invention discloses a method for training a neural network to control game role behaviors based on a genetic algorithm. According to the method, necessary input information is obtained from each frame in a game engine through population samples; behavior input information is obtained through a neural network; the samples and the environment are interacted to update a fitness function, after acertain time, the population samples are divided into two sample populations according to a certain proportion and multiplied in two modes respectively, progenies of the two populations are combined into one population, and the population more adapting to the environment is obtained along with ceaseless training. According to the method, the neural network is used for processing environment input;more comprehensive information can be obtained; compared with the prior art, the method is more flexible in action without behavior pattern limitation, action strategy optimization can be automatically carried out according to actions of players in the actual application process, the genetic algorithm used in the method enables the search range to be wider, the sample diversity is increased, andthe used multi-genetic strategy makes up for the defect that local optimum is likely to enter too early to a certain extent.

Description

technical field [0001] The invention belongs to the field of game character control, and in particular relates to a method for training a neural network based on a genetic algorithm to control the behavior of a game character. Background technique [0002] With the development of game 3D technology and physical simulation technology, the future game industry that can be seen will gradually occupy people's leisure time. In recent years, some game masterpieces have also slowly entered the market, and whether the behavior of autonomous characters in the game has the ability to change behavior patterns has always been a concern of players. [0003] At present, most game AIs are mostly implemented through finite state machines. The realization of this method requires designers to design a complete set of behavioral logic, and at the same time, various transition conditions need to be considered. The advantage lies in stability, and the disadvantages are also obvious. The behavio...

Claims

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Application Information

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IPC IPC(8): G06N3/00A63F13/57
CPCA63F13/57G06N3/006
Inventor 王宪保杨传宇朱啸咏吴飞腾
Owner ZHEJIANG UNIV OF TECH
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