Evolutionary Strategies

Mortar: Evolving Mechanics for Automatic Game Design
We present Mortar, a system for autonomously evolving game mechanics for automatic game design. Game mechanics define the rules and …
Muhammad Nasir, Yuchen Li, Steven James, Julian Togelius
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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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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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Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content
In this work, we consider the problem of procedural content generation for video game levels. Prior approaches have relied on …
Nicholas Muir, Steven James
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