Neuroevolution

Using NEAT to Learn Operators for Flexible Boolean Composition within Reinforcement Learning
Skill composition is a growing area of interest within Reinforcement Learning (RL) research. For example, if designing a robot for …
Amir Esterhuysen, Steven James, Geraud Nangue Tasse, Benjamin Rosman, Jonathan Shock
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LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization
Large language models (LLMs) have emerged as powerful tools capable of accomplishing a broad spectrum of tasks. Their abilities span …
Muhammad Nasir, Sam Earle, Christopher Cleghorn, Steven James, Julian Togelius
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Hierarchically Composing Level Generators for the Creation of Complex Structures
Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to …
Michael Beukman, Manuel Fokam, Marcel Kruger, Guy Axelrod, Muhammad Nasir, Branden Ingram, Benjamin Rosman, Steven James
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Augmentative Topology Agents For Open-ended Learning
We tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents while also evolving …
Muhammad Nasir, Michael Beukman, Steven James, Christopher Cleghorn
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Augmentative Topology Agents For Open-ended Learning
In this work, we tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents and increasingly …
Muhammad Nasir, Steven James, Christopher Cleghorn
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Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game Levels
Procedurally generated video game content has the potential to drastically reduce the content creation budget of game developers and …
Michael Beukman, Christopher Cleghorn, Steven James
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Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game Levels