Optimizing the Neural Architecture of Reinforcement Learning Agents

• URL: http://arxiv.org/abs/2011.14632v3
• Date: Wed, 28 Apr 2021 10:21:47 GMT
• Title: Optimizing the Neural Architecture of Reinforcement Learning Agents
• Authors: N. Mazyavkina, S. Moustafa, I. Trofimov, E. Burnaev
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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