在多人角色扮演游戏中控制非可玩角色行为的机器学习方法

Roman Budnyk, V. Yakovyna
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摘要

本文讨论了在多人角色扮演游戏中为非玩家角色开发控制系统的问题。电子游戏和RPG(角色扮演游戏)领域的商业项目很少使用机器学习模型来实现角色行为。最常见的方法是使用原始的预编程规则,或者实现有限状态机。这种方法破坏了玩真实生物的沉浸感,因为各种预定义的规则使角色变得可预测。优秀的游戏AI应该给玩家一种与真实角色互动的印象,让他们做出各种各样的决定,有时是不可预测的。为了实现这一目标,本文介绍了一种将各种机器学习模型与传统有限状态机结合使用的方法。早期开发的电子游戏被用作解决问题的基础。本文对电子游戏AI领域的现有工作进行了分析。其次,介绍了控制系统的实现。该系统使用了几个经过选择和成功训练的机器学习模型。测试了大量的模型,最终选择了决策树模型和神经网络模型,因为它们产生了最好的结果。然后描述了涉及机器学习模型的控制系统的开发和实现过程。介绍了该模型的教学方法,并对所取得的效果进行了分析。为了评估结果,不同的模型在测试战中相互比较。决策树模型的结果比传统的有限状态机略好。与此同时,神经网络的表现明显更好,击败其他模型的频率要高得多。已经取得的结果可以进一步发展,利用更复杂的模型和改进训练方法,这将导致更复杂的字符。在游戏中出现这样的角色将从质量上改善游戏玩法。获得的系统被集成到视频游戏中,这有可能成为商业产品。
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Machine learning methods for control of non-playable characters behaviour in multiplayer RPG
This article covers the problem of developing a control system for non-player characters in a multiplayer RPG. Commercial projects in the field of videogames and RPG (Role-Playing Game) projects in particular seldom use machine learning models for the implementation of character behaviour. The most common approach is to use primitive preprogrammed rules, or to implement a finite state machine. Such approaches ruin the immersion of playing with real creatures, since various predefined rules make the characters predictable. A good game AI is supposed to give the player an impression of interacting with real characters, that make various decisions, sometimes unpredictable. To achieve this goal, this article covers an approach with using various machine learning models in conjunction with a traditional finite state machine. A videogame developed earlier is used as the basis for problem solution. The article conducts an analysis of the existing works in the field of videogame AI. Next, the implementation of control system is described. This system utilizes a couple selected and successfully trained machine learning models. A multitude of models were tested, eventually a decision tree model and a neural network were selected, since they yielded the best results. The process of development and implementation of a control system involving machine learning models is then described. The approaches of teaching such models are described, and finally the achieved results are analyzed. To gauge the results, different models were compared against each other in test battles. The decision tree model showed results slightly better than the traditional finite state machine. Meanwhile, the neural network performed significantly better, beating other models far more often. Achieved results can be developed further, utilizing more complex models and improving training methods, which will result in even more sophisticated characters. Presence of such characters in the game will qualitatively improve the gameplay. Obtained system was integrated into the videogame, that may potentially become a commercial product.
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