Leveraging Structures in Evolutionary Neural Policy Search

Training agents to perform complex tasks like driving a car, mastering a video game, or controlling a robot to walk presents a significant challenge when expert demonstrations are not available. In nature, complex behaviors and characteristics can emerge through evolution, as animals adapt to their environments and problems over generations.

Authors

Publication Details

Journal
ACM SIGEVOlution
Published
2025-03-01
DOI
https://doi.org/10.1145/3733097.3733101
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
0.00
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article

Leveraging Structures in Evolutionary Neural Policy Search

Paul Templier
ACM SIGEVOlution
Reinforcement Learning in Robotics
article

Leveraging Structures in Evolutionary Neural Policy Search

Paul Templier
article en

Abstract

Training agents to perform complex tasks like driving a car, mastering a video game, or controlling a robot to walk presents a significant challenge when expert demonstrations are not available. In nature, complex behaviors and characteristics can emerge through evolution, as animals adapt to their environments and problems over generations.

ACM SIGEVOlutionVol. 18(1)
Openalex Percentile: Top 96%
Reinforcement Learning in Robotics
DOISourceOpenAlex
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Leveraging Structures in Evolutionary Neural Policy Search — Paul Templier · ACM SIGEVOlution (2025) | TGRS Research Map | TGRS