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Found 10 papers, 6 papers with code
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization
Quality-Diversity has emerged as a powerful family of evolutionary algorithms that generate diverse populations of high-performing solutions by implementing local competition principles inspired by biological evolution.
Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity
Quality-Diversity is a family of evolutionary algorithms that generate diverse, high-performing solutions through local competition principles inspired by natural evolution.
Scaling Policy Gradient Quality-Diversity with Massive Parallelization via Behavioral Variations
While successful at scaling ME for neuroevolution, these methods often suffer from slow training speeds, or difficulties in scaling with massive parallelization due to high computational demands or reliance on centralized actor-critic training.
Preference-Conditioned Gradient Variations for Multi-Objective Quality-Diversity
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.
CAX: Cellular Automata Accelerated in JAX
Cellular automata have become a cornerstone for investigating emergence and self-organization across diverse scientific disciplines, spanning neuroscience, artificial life, and theoretical physics.
Toward Artificial Open-Ended Evolution within Lenia using Quality-Diversity
Combined with Lenia, a family of continuous cellular automata, we demonstrate that our method is able to evolve a diverse population of lifelike self-organizing autonomous patterns.
OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code
Overall, OMNI-EPIC can endlessly create learnable and interesting environments, further propelling the development of self-improving AI systems and AI-Generating Algorithms.
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations.
Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning
In this work, we introduce DCRL-MAP-Elites, an extension of DCG-MAP-Elites that utilizes the descriptor-conditioned actor as a generative model to produce diverse solutions, which are then injected into the offspring batch at each generation.
MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy
Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfully applied to a variety of domains and particularly in evolutionary robotics.
