论文标题

使用技能评级作为甘纳斯进化的健身

Using Skill Rating as Fitness on the Evolution of GANs

论文作者

Costa, Victor, Lourenço, Nuno, Correia, João, Machado, Penousal

论文摘要

生成对抗网络(GAN)是一个对抗模型,在生成任务上取得了令人印象深刻的结果。尽管有相关的结果,但GAN还是在稳定性方面面临一些挑战,这使得培训通常是击中的过程。为了克服这些挑战,提出了一些改进,以更好地处理模型的内部特征,例如替代性损失函数或生成器和歧视器使用的神经网络上的体系结构变化。最近的著作提出了在GAN培训中使用进化算法,旨在解决这些挑战并提供一种自动方法来寻找良好模型。在这种情况下,Coegan提议使用协同进化和神经进化来协调gan的训练。但是,先前的实验检测到用于指导进化的某些适应性功能并不理想。在这项工作中,我们建议在Coegan方法中使用基于游戏的健身功能的评估。技能评级是量化游戏中玩家的技能的指标,并且已经被用来评估甘斯。我们使用进化算法中的技能评级来训练甘恩。结果表明,技能评级可用作适应性,以指导科甘的演变,而无需外部评估者的依赖性。

Generative Adversarial Networks (GANs) are an adversarial model that achieved impressive results on generative tasks. In spite of the relevant results, GANs present some challenges regarding stability, making the training usually a hit-and-miss process. To overcome these challenges, several improvements were proposed to better handle the internal characteristics of the model, such as alternative loss functions or architectural changes on the neural networks used by the generator and the discriminator. Recent works proposed the use of evolutionary algorithms on GAN training, aiming to solve these challenges and to provide an automatic way to find good models. In this context, COEGAN proposes the use of coevolution and neuroevolution to orchestrate the training of GANs. However, previous experiments detected that some of the fitness functions used to guide the evolution are not ideal. In this work we propose the evaluation of a game-based fitness function to be used within the COEGAN method. Skill rating is a metric to quantify the skill of players in a game and has already been used to evaluate GANs. We extend this idea using the skill rating in an evolutionary algorithm to train GANs. The results show that skill rating can be used as fitness to guide the evolution in COEGAN without the dependence of an external evaluator.

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