Our society is increasingly fond of computational tools. This phenomenon has greatly increased over the past decade following, among other factors, the emergence of a new Artificial Intelligence paradigm. Specifically, the coupling of two algorithmic techniques, Deep Neural Networks and Stochastic Gradient Descent, thrusted by an exponentially increasing computing capacity, has and is continuing to become a major asset in many modern technologies. However, as progress takes its course, some still wonder whether other methods could similarly or even more greatly benefit from these various hardware advances. In order to further this study, we delve in this thesis into Evolutionary Algorithms and their application to Dynamic Neural Networks, two techniques which despite enjoying many advantageous properties have yet to find their niche in contemporary Artificial Intelligence. We find that by elaborating new methods while exploiting strong computational resources, it becomes possible to develop strongly performing agents on a variety of benchmarks but also some other agents behaving very similarly to human subjects on the video game Shinobi III : Return of The Ninja Master, typical complex tasks previously out of reach for non-gradient-based optimization.

翻译：我们的社会正日益依赖计算工具。这一现象在过去十年中显著加剧，其背后因素之一便是人工智能新范式的兴起。具体而言，深度神经网络与随机梯度下降这两种算法技术的结合，在呈指数级增长的算力推动下，已并持续成为众多现代技术的核心支撑。然而，随着技术演进，仍有人质疑其他方法能否同样甚至更大程度地从这些硬件进步中受益。为深入探究此问题，本文研究了进化算法及其在动态神经网络中的应用——这两种技术虽具备诸多优势特性，但在当代人工智能领域尚未找到合适定位。研究发现，通过设计新方法并充分利用强大计算资源，不仅能开发出一系列基准测试中表现优异的智能体，还能在《忍者龙剑传III：忍者大师归来》这款视频游戏中构建与人类玩家行为高度相似的智能体。这标志着此前非梯度优化方法难以企及的复杂任务实现了突破性进展。