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.

中文翻译：

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优化强化学习代理的神经体系结构

强化学习（RL）在过去几年中取得了显着进步。向前迈出的最重要的一步之一就是神经网络的广泛应用。但是，这些神经网络的体系结构通常是手动构建的。在这项工作中，我们研究了最近提出的用于优化RL代理的体系结构的神经体系结构搜索（NAS）方法。我们以Atari基准进行实验，并得出结论，现代NAS方法发现RL代理的体系结构优于手动选择的体系。