6 citations · 6 across the 8 of their papers we have counts for
9 papers
Evolving Many Worlds: Towards Open-Ended Discovery in Petri Dish NCA via Population-Based Training
Uljad Berdica, Jakob Foerster, Frank Hutter +1
The generation of sustained, open-ended complexity from local interactions remains a fundamental challenge in artificial life. Differentiable multi-agent systems, such as Petri Dis…
When Do We Need LLMs? A Diagnostic for Language-Driven Bandits
Uljad Berdica, Fernando Acero, Anton Ipsen +3
We study Contextual Multi-Armed Bandits (CMABs) for non-episodic decision-making problems where the context includes both textual and numerical information (e.g., recommendation sy…
DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning
Alexander D. Goldie, Zilin Wang, Adrian Hayler +17
Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and evaluate algorithm discovery systems ha…
Evolution Strategies at the Hyperscale
Bidipta Sarkar, Mattie Fellows, Juan Agustin Duque +17
Evolution Strategies (ES) is a class of powerful black-box optimisation methods that are highly parallelisable and can handle non-differentiable and noisy objectives. However, naïv…
Intent Factored Generation: Unleashing the Diversity in Your Language Model
Eltayeb Ahmed, Uljad Berdica, Martha Elliott +2
Obtaining multiple meaningfully diverse, high quality samples from Large Language Models for a fixed prompt remains an open challenge. Current methods for increasing diversity ofte…
Fully Offline Reinforcement Learning
Mattie Fellows, Clarisse Wibault, Uljad Berdica +3
Offline RL (ORL) promises safe and sample-efficient deployment but existing methods rely on undocumented online interactions for hyperparameter tuning and lack reliable fully offli…
A Clean Slate for Offline Reinforcement Learning
Matthew Thomas Jackson, Uljad Berdica, Jarek Liesen +2
Progress in offline reinforcement learning (RL) has been impeded by ambiguous problem definitions and entangled algorithmic designs, resulting in inconsistent implementations, insu…
Reinforcement Learning Controllers for Soft Robots using Learned Environments
Uljad Berdica, Matthew Jackson, Niccolò Enrico Veronese +2
Soft robotic manipulators offer operational advantage due to their compliant and deformable structures. However, their inherently nonlinear dynamics presents substantial challenges…
Mobile 3D Printing Robot Simulation with Viscoelastic Fluids
Uljad Berdica, Yuewei Fu, Yuchen Liu +2
The system design and algorithm development of mobile 3D printing robots need a realistic simulation. They require a mobile robot simulation platform to interoperate with a physics…