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pro vyhledávání: '"Cully, P."'
In a variety of domains, from robotics to finance, Quality-Diversity algorithms have been used to generate collections of both diverse and high-performing solutions. Multi-Objective Quality-Diversity algorithms have emerged as a promising approach fo
Externí odkaz:
http://arxiv.org/abs/2411.12433
Autor:
Faldor, Maxence, Cully, Antoine
Cellular automata have become a cornerstone for investigating emergence and self-organization across diverse scientific disciplines, spanning neuroscience, artificial life, and theoretical physics. However, the absence of a hardware-accelerated cellu
Externí odkaz:
http://arxiv.org/abs/2410.02651
Quality-Diversity (QD) algorithms have exhibited promising results across many domains and applications. However, uncertainty in fitness and behaviour estimations of solutions remains a major challenge when QD is used in complex real-world applicatio
Externí odkaz:
http://arxiv.org/abs/2409.13315
Autor:
Faldor, Maxence, Cully, Antoine
From the formation of snowflakes to the evolution of diverse life forms, emergence is ubiquitous in our universe. In the quest to understand how complexity can arise from simple rules, abstract computational models, such as cellular automata, have be
Externí odkaz:
http://arxiv.org/abs/2406.04235
Open-ended and AI-generating algorithms aim to continuously generate and solve increasingly complex tasks indefinitely, offering a promising path toward more general intelligence. To accomplish this grand vision, learning must occur within a vast arr
Externí odkaz:
http://arxiv.org/abs/2405.15568
Evolutionary Algorithms (EA) have been successfully used for the optimization of neural networks for policy search, but they still remain sample inefficient and underperforming in some cases compared to gradient-based reinforcement learning (RL). Var
Externí odkaz:
http://arxiv.org/abs/2405.04322
Evolution Strategies (ES) are effective gradient-free optimization methods that can be competitive with gradient-based approaches for policy search. ES only rely on the total episodic scores of solutions in their population, from which they estimate
Externí odkaz:
http://arxiv.org/abs/2405.04308
Quality-Diversity (QD) approaches are a promising direction to develop open-ended processes as they can discover archives of high-quality solutions across diverse niches. While already successful in many applications, QD approaches usually rely on co
Externí odkaz:
http://arxiv.org/abs/2404.15794
Autor:
Janmohamed, Hannah, Wolinska, Marta, Surana, Shikha, Pierrot, Thomas, Walsh, Aron, Cully, Antoine
Crystal structures are indispensable across various domains, from batteries to solar cells, and extensive research has been dedicated to predicting their properties based on their atomic configurations. However, prevailing Crystal Structure Predictio
Externí odkaz:
http://arxiv.org/abs/2403.17164
A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations. Over the past decade, advancements in deep reinforcement learning have led to groundbreaking achievements to solve complex
Externí odkaz:
http://arxiv.org/abs/2403.09930