3 citations · 7 across the 14 of their papers we have counts for
15 papers
A team of Gemini 4 Argon agents beats Minecraft
Maxence Faldor, Satinder Singh, Roberta Raileanu
We placed four Gemini 4 Argon agents in a sandboxed Minecraft survival world and gave them one goal: defeat the Ender Dragon. Each agent controlled a player via an open-source libr…
Dreaming in Code for Curriculum Learning in Open-Ended Worlds
Konstantinos Mitsides, Maxence Faldor, Antoine Cully
Open-ended learning frames intelligence as emerging from continual interaction with an ever-expanding space of environments. While recent advances have utilized foundation models t…
Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization
Robert Tjarko Lange, Qi Sun, Aaditya Prasad +3
Recent advances in large language models (LLMs) demonstrate their effectiveness in scaling test-time compute for software engineering tasks. However, these approaches often focus o…
From Tabula Rasa to Emergent Abilities: Discovering Robot Skills via Real-World Unsupervised Quality-Diversity
Luca Grillotti, Lisa Coiffard, Oscar Pang +2
Autonomous skill discovery aims to enable robots to acquire diverse behaviors without explicit supervision. Learning such behaviors directly on physical hardware remains challengin…
A Path to Universal Neural Cellular Automata
Gabriel Béna, Maxence Faldor, Dan F. M. Goodman +1
Cellular automata have long been celebrated for their ability to generate complex behaviors from simple, local rules, with well-known discrete models like Conway's Game of Life pro…
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization
Maxence Faldor, Robert Tjarko Lange, Antoine Cully
Quality-Diversity has emerged as a powerful family of evolutionary algorithms that generate diverse populations of high-performing solutions by implementing local competition princ…
Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity
Ryan Bahlous-Boldi, Maxence Faldor, Luca Grillotti +4
Quality-Diversity is a family of evolutionary algorithms that generate diverse, high-performing solutions through local competition principles inspired by natural evolution. While…
Scaling Policy Gradient Quality-Diversity with Massive Parallelization via Behavioral Variations
Konstantinos Mitsides, Maxence Faldor, Antoine Cully
Quality-Diversity optimization comprises a family of evolutionary algorithms aimed at generating a collection of diverse and high-performing solutions. MAP-Elites (ME), a notable e…
Preference-Conditioned Gradient Variations for Multi-Objective Quality-Diversity
Hannah Janmohamed, Maxence Faldor, Thomas Pierrot +1
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-Objectiv…
CAX: Cellular Automata Accelerated in JAX
Maxence Faldor, Antoine Cully
Cellular automata have become a cornerstone for investigating emergence and self-organization across diverse scientific disciplines. However, the absence of a hardware-accelerated…
Toward Artificial Open-Ended Evolution within Lenia using Quality-Diversity
Maxence Faldor, Antoine Cully
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 ru…
OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code
Maxence Faldor, Jenny Zhang, Antoine Cully +1
Open-ended and AI-generating algorithms aim to continuously generate and solve increasingly complex tasks indefinitely, offering a promising path toward more general intelligence.…
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
Luca Grillotti, Maxence Faldor, Borja G. León +1
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 reinforce…
Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning
Maxence Faldor, Félix Chalumeau, Manon Flageat +1
A hallmark of intelligence is the ability to exhibit a wide range of effective behaviors. Inspired by this principle, Quality-Diversity algorithms, such as MAP-Elites, are evolutio…
MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy
Maxence Faldor, Félix Chalumeau, Manon Flageat +1
Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfu…