Leveraging Structures in Evolutionary Neural Policy Search

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@Article{Templier:2025:sigevolution,

• author = "Paul Templier",
• title = "Leveraging Structures in Evolutionary Neural Policy Search",
• journal = "SIGEVOlution",
• year = "2025",
• volume = "18",
• number = "1",
• articleno = "4",
• month = mar,
• note = "PhD Dissertation Reports",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, GENE, Genetic Drift Regularization, landscape analysis, Just Enough Diversity, JEDi, ANN, Evolution Strategies, Quality-Diversity, Reinforcement Learning, Walker2D, ISAE-Supaero",
• publisher = "Association for Computing Machinery",
• DOI = "10.1145/3733097.3733101",
• 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.",
• notes = "write up of \cite{templier2024synergies}",

}

Genetic Programming entries for Paul Templier

Citations