Parallel Distributed Implementation of Neuroevolution of Augmenting Topologies in Continuous Control Tasks

I. Achour, A. Doroshenko
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Abstract

This paper proposes a novel distributed implementation of neuroevolution of augmenting topologies method, which, considering the availability of sufficient computational resources, allows drastically speed up the process of optimal neural network configuration search. The proposed solution includes batch genome evaluation for the purpose of performance optimization, fair, and even computational resources usage. The benchmarking shows that the generated neural networks evaluation process can give orders of magnitude increase of efficiency on the demonstrated continuous control task and computational environment.
连续控制任务中增强拓扑神经进化的并行分布式实现
本文提出了一种基于增强拓扑的神经进化分布式实现方法,该方法在考虑足够计算资源的情况下,大大加快了神经网络最优配置搜索的速度。提出的解决方案包括批量基因组评估,以实现性能优化、公平和均匀的计算资源使用。基准测试表明,所生成的神经网络评估过程在演示的连续控制任务和计算环境中可以使效率提高几个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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