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pro vyhledávání: '"Lim, Bryan"'
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
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
Many applications in Reinforcement Learning (RL) usually have noise or stochasticity present in the environment. Beyond their impact on learning, these uncertainties lead the exact same policy to perform differently, i.e. yield different return, from
Externí odkaz:
http://arxiv.org/abs/2312.07178
Autor:
Ingvarsson, Garðar, Samvelyan, Mikayel, Lim, Bryan, Flageat, Manon, Cully, Antoine, Rocktäschel, Tim
In many real-world systems, such as adaptive robotics, achieving a single, optimised solution may be insufficient. Instead, a diverse set of high-performing solutions is often required to adapt to varying contexts and requirements. This is the realm
Externí odkaz:
http://arxiv.org/abs/2311.01829
Autor:
Chalumeau, Felix, Lim, Bryan, Boige, Raphael, Allard, Maxime, Grillotti, Luca, Flageat, Manon, Macé, Valentin, Flajolet, Arthur, Pierrot, Thomas, Cully, Antoine
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization purposes, ranging from black-box optimization to continuous control.
Externí odkaz:
http://arxiv.org/abs/2308.03665
Learning algorithms, like Quality-Diversity (QD), can be used to acquire repertoires of diverse robotics skills. This learning is commonly done via computer simulation due to the large number of evaluations required. However, training in a virtual en
Externí odkaz:
http://arxiv.org/abs/2304.12080
Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space. However, in the presence of unpredictable noise, the fitness and descriptor of the same
Externí odkaz:
http://arxiv.org/abs/2304.03672
The synergies between Quality-Diversity (QD) and Deep Reinforcement Learning (RL) have led to powerful hybrid QD-RL algorithms that have shown tremendous potential, and brings the best of both fields. However, only a single deep RL algorithm (TD3) ha
Externí odkaz:
http://arxiv.org/abs/2303.06164
With the development of fast and massively parallel evaluations in many domains, Quality-Diversity (QD) algorithms, that already proved promising in a large range of applications, have seen their potential multiplied. However, we have yet to understa
Externí odkaz:
http://arxiv.org/abs/2303.06137