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Analysing the Effect of Latent Space Mutation Strategies for PCGML
Procedural Content Generation Via Machine Learning (PCGML), describes an evolving area of research, that incorporates the use of …
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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 …
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Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning
It is desirable for an agent to be able to solve a rich variety of problems that can be specified through language in the same …
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Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
While reinforcement learning has achieved remarkable successes in several domains, its real-world application is limited due to many …
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Synthesizing Navigation Abstractions for Planning with Portable Manipulation Skills
We address the problem of efficiently learning high-level abstractions for task-level robot planning. Existing approaches require large …
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Creating Diverse Play-Style-Centric Agents through Behavioural Cloning
Developing diverse and realistic agents in terms of behaviour and skill is crucial for game developers to enhance player satisfaction …
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Hierarchical WaveFunction Collapse
Video game developers are increasingly utilising procedural content generation (PCG) techniques in order to generate more content far …
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A Comparison of Visual Place Recognition Methods using a Mobile Robot in an Indoor Environment
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Overlooked Implications of the Reconstruction Loss for VAE Disentanglement
Learning disentangled representations with variational autoencoders (VAEs) is often attributed to the regularisation component of the …
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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 …
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