论文标题
优化强化学习者的神经结构
Optimizing the Neural Architecture of Reinforcement Learning Agents
论文作者
论文摘要
在过去的几年中，强化学习（RL）取得了重大进展。前进的最重要步骤之一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神经体系结构搜索（NAS）方法，用于优化RL剂的体系结构。我们对Atari基准进行实验，并得出结论，现代的NAS方法发现RL代理的体系结构的表现优于手动选择的架构。
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.
