20 citations · 38 across the 12 of their papers we have counts for
11 papers · 1 filter
Goal-Conditioned Agents that Learn Everything All at Once
Michael Matthews, Matthew Jackson, Michael Beukman +5
A goal-conditioned reinforcement learning agent exploring an environment will see a wealth of information throughout a trajectory, most of which is discarded when only performing o…
Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI
Julien Pourcel, Cédric Colas, Pierre-Yves Oudeyer
Many program synthesis tasks prove too challenging for even state-of-the-art language models to solve in single attempts. Search-based evolutionary methods offer a promising altern…
ACES: Generating Diverse Programming Puzzles with with Autotelic Generative Models
Julien Pourcel, Cédric Colas, Gaia Molinaro +2
The ability to invent novel and interesting problems is a remarkable feature of human intelligence that drives innovation, art, and science. We propose a method that aims to automa…
Language-Conditioned Goal Generation: a New Approach to Language Grounding for RL
Cédric Colas, Ahmed Akakzia, Pierre-Yves Oudeyer +2
In the real world, linguistic agents are also embodied agents: they perceive and act in the physical world. The notion of Language Grounding questions the interactions between lang…
Deep Sets for Generalization in RL
Tristan Karch, Cédric Colas, Laetitia Teodorescu +2
This paper investigates the idea of encoding object-centered representations in the design of the reward function and policy architectures of a language-guided reinforcement learni…
Automatic Curriculum Learning For Deep RL: A Short Survey
Rémy Portelas, Cédric Colas, Lilian Weng +2
Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL).These methods shape the learning trajectories of agents by cha…