Reinforcement learning (RL) enjoyed significant progress over the last years. One of the most important steps forward was the wide application of neural networks. However, architectures of these neural networks are typically constructed manually. In this work, we study recently proposed neural architecture search (NAS) methods for optimizing the architecture of RL agents. We carry out experiments on the Atari benchmark and conclude that modern NAS methods find architectures of RL agents outperforming a manually selected one.

翻译：过去几年来,强化学习(RL)取得了显著进展,其中最重要的进步之一是神经网络的广泛应用,然而,这些神经网络的结构通常是人工建造的。在这项工作中,我们最近研究了为优化RL代理机构的结构而提出的神经结构搜索方法。我们进行了有关Atari基准的实验,并得出结论,现代NAS方法发现RL代理机构的建筑比人工选择的要好。