1 citations · 2 across the 10 of their papers we have counts for
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HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents
Thomas Carta, Clément Romac, Loris Gaven +3
We study goal-conditioned reinforcement learning in partially observable environments with sparse rewards and large, structured goal spaces. In such settings, complex goals often r…
Imagine Beyond! Distributionally Robust Auto-Encoding for State Space Coverage in Online Reinforcement Learning
Nicolas Castanet, Olivier Sigaud, Sylvain Lamprier
Goal-Conditioned Reinforcement Learning (GCRL) enables agents to autonomously acquire diverse behaviors, but faces major challenges in visual environments due to high-dimensional,…
Offline Learning of Controllable Diverse Behaviors
Mathieu Petitbois, Rémy Portelas, Sylvain Lamprier +1
Imitation Learning (IL) techniques aim to replicate human behaviors in specific tasks. While IL has gained prominence due to its effectiveness and efficiency, traditional methods o…
A Transformer Model for Predicting Chemical Products from Generic SMARTS Templates with Data Augmentation
Derin Ozer, Sylvain Lamprier, Thomas Cauchy +2
The accurate prediction of chemical reaction outcomes is a major challenge in computational chemistry. Current models rely heavily on either highly specific reaction templates or t…
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…