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Papers/Optimizing the Neural Architecture of Reinforcement Learni...

Optimizing the Neural Architecture of Reinforcement Learning Agents

N. Mazyavkina, S. Moustafa, I. Trofimov, E. Burnaev

2020-11-30Reinforcement LearningAtari GamesNeural Architecture SearchMeta Reinforcement Learningreinforcement-learning
PaperPDFCode(official)

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.

Results

TaskDatasetMetricValueModel
Atari GamesAtari 2600 FreewayScore22ENAS
Atari GamesAtari 2600 FreewayScore22SPOS
Atari GamesAtari 2600 BreakoutScore180.6SPOS
Atari GamesAtari 2600 BreakoutScore161.1ENAS Search space 1
Atari GamesAtari 2600 BreakoutScore144.4SPOS Search space 1
Atari GamesAtari 2600 BreakoutScore91.4ENAS
Video GamesAtari 2600 FreewayScore22ENAS
Video GamesAtari 2600 FreewayScore22SPOS
Video GamesAtari 2600 BreakoutScore180.6SPOS
Video GamesAtari 2600 BreakoutScore161.1ENAS Search space 1
Video GamesAtari 2600 BreakoutScore144.4SPOS Search space 1
Video GamesAtari 2600 BreakoutScore91.4ENAS

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