Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control

Jörg K. H. Franke; Gregor Köhler; Noor Awad; Frank Hutter

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arXiv · 1910.12824

Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control

Jörg K. H. Franke·Gregor Köhler·Noor Awad·Frank Hutter

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Abstract

Current Deep Reinforcement Learning algorithms still heavily rely on handcrafted neural network architectures. We propose a novel approach to automatically find strong topologies for continuous control tasks while only adding a minor overhead in terms of interactions in the environment. To achieve this, we combine Neuroevolution techniques with off-policy training and propose a novel architecture mutation operator. Experiments on five continuous control benchmarks show that the proposed Actor-Critic Neuroevolution algorithm often outperforms the strong Actor-Critic baseline and is capable of automatically finding topologies in a sample-efficient manner which would otherwise have to be found by expensive architecture search.

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BibTeX RIS

Jörg K. H. Franke, Gregor Köhler, Noor Awad, Frank Hutter. 2019-10-28. Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control. https://arxiv.org/abs/1910.12824

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