42 citations · 42 across the 5 of their papers we have counts for
6 papers · 1 filter
Discovering Adaptive Transmission Programs for Collective Innovation
Cédric Colas, Jérémy Perez, Eleni Nisioti +4
Human collective intelligence depends on transmission processes: who shares what with whom, how, and when. While these processes emerge from individual cognition, they can also be…
WorldLLM: Improving LLMs' world modeling using curiosity-driven theory-making
Guillaume Levy, Cedric Colas, Pierre-Yves Oudeyer +2
Large Language Models (LLMs) possess general world knowledge but often struggle to generate precise predictions in structured, domain-specific contexts such as simulations. These l…
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Cédric Colas, Pierre Fournier, Olivier Sigaud +2
In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a l…
Expedition & Expansion: Leveraging Semantic Representations for Goal-Directed Exploration in Continuous Cellular Automata
Sina Khajehabdollahi, Gautier Hamon, Marko Cvjetko +3
Discovering diverse visual patterns in continuous cellular automata (CA) is challenging due to the vastness and redundancy of high-dimensional behavioral spaces. Traditional explor…
MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces
Loris Gaven, Thomas Carta, Clément Romac +4
Open-ended learning agents must efficiently prioritize goals in vast possibility spaces, focusing on those that maximize learning progress (LP). When such autotelic exploration is…
A Definition of Open-Ended Learning Problems for Goal-Conditioned Agents
Olivier Sigaud, Gianluca Baldassarre, Cedric Colas +5
A lot of recent machine learning research papers have ``open-ended learning'' in their title. But very few of them attempt to define what they mean when using the term. Even worse,…