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arXiv:1806.05695cs.NE

Evolving simple programs for playing Atari games

Dennis G Wilson, Sylvain Cussat-Blanc, Hervé Luga, Julian F Miller

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

Cartesian Genetic Programming (CGP) has previously shown capabilities in image processing tasks by evolving programs with a function set specialized for computer vision. A similar approach can be applied to Atari playing. Programs are evolved using mixed type CGP with a function set suited for matrix operations, including image processing, but allowing for controller behavior to emerge. While the programs are relatively small, many controllers are competitive with state of the art methods for the Atari benchmark set and require less training time. By evaluating the programs of the best evolved individuals, simple but effective strategies can be found.

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DOI
Subjects
cs.NENeural and Evolutionary Computing cs.AIArtificial Intelligence

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Sep 2018 to Sep 2018. Found in 4 HN submissions from Jul 2018 to Oct 2018. Open submission discussions.

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  1. Ask HN: Is anyone working on ML based code/program generation? 1 comment

    • Yes, you will want to research "program induction" and "program synthesis". For a review of recent work in both, see https://arxiv.org/abs/1802.02353 For program synthesis applied to the "Atari" deep learning benchmark check out https://arxiv.org/abs/1806.05695

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