30 citations · 90 across the 39 of their papers we have counts for
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Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces
Bryon Tjanaka, Henry Chen, Matthew C. Fontaine +1
Quality diversity (QD) optimization searches for a collection of solutions that optimize an objective while attaining diverse outputs of a user-specified, vector-valued measure fun…
Soft Quality-Diversity Optimization
Saeed Hedayatian, Stefanos Nikolaidis
Quality-Diversity (QD) algorithms constitute a branch of optimization that is concerned with discovering a diverse and high-quality set of solutions to an optimization problem. Cur…
AutoQD: Automatic Discovery of Diverse Behaviors with Quality-Diversity Optimization
Saeed Hedayatian, Stefanos Nikolaidis
Quality-Diversity (QD) algorithms have shown remarkable success in discovering diverse, high-performing solutions, but rely heavily on hand-crafted behavioral descriptors that cons…
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
Robby Costales, Stefanos Nikolaidis
The wider application of end-to-end learning methods to embodied decision-making domains remains bottlenecked by their reliance on a superabundance of training data representative…
Density Descent for Diversity Optimization
David H. Lee, Anishalakshmi V. Palaparthi, Matthew C. Fontaine +2
Diversity optimization seeks to discover a set of solutions that elicit diverse features. Prior work has proposed Novelty Search (NS), which, given a current set of solutions, seek…
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Sumeet Batra, Bryon Tjanaka, Matthew C. Fontaine +3
Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning. Quality Diversity Reinforcement…