no code implementations • 3 Oct 2022 • Tristan Karch, Yoann Lemesle, Romain Laroche, Clément Moulin-Frier, Pierre-Yves Oudeyer
In this paper, we investigate whether artificial agents can develop a shared language in an ecological setting where communication relies on a sensory-motor channel.
no code implementations • 2 Jun 2022 • Cédric Colas, Tristan Karch, Clément Moulin-Frier, Pierre-Yves Oudeyer
Building autonomous agents able to grow open-ended repertoires of skills across their lives is a fundamental goal of artificial intelligence (AI).
1 code implementation • ICLR 2022 • Paul Barde, Tristan Karch, Derek Nowrouzezahrai, Clément Moulin-Frier, Christopher Pal, Pierre-Yves Oudeyer
ABIG results in a low-level, high-frequency, guiding communication protocol that not only enables an architect-builder pair to solve the task at hand, but that can also generalize to unseen tasks.
1 code implementation • NeurIPS 2021 • Tristan Karch, Laetitia Teodorescu, Katja Hofmann, Clément Moulin-Frier, Pierre-Yves Oudeyer
While there is an extended literature studying how machines can learn grounded language, the topic of how to learn spatio-temporal linguistic concepts is still largely uncharted.
no code implementations • 17 Dec 2020 • Cédric Colas, Tristan Karch, Olivier Sigaud, Pierre-Yves Oudeyer
Developmental RL is concerned with the use of deep RL algorithms to tackle a developmental problem -- the $intrinsically$ $motivated$ $acquisition$ $of$ $open$-$ended$ $repertoires$ $of$ $skills$.
2 code implementations • NeurIPS 2020 • Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Dominey, Pierre-Yves Oudeyer
We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.
no code implementations • ICML Workshop LaReL 2020 • Tristan Karch, Nicolas Lair, Cédric Colas, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Ford Dominey, Pierre-Yves Oudeyer
We introduce the Playground environment and study how this form of goal imagination improves generalization and exploration over agents lacking this capacity.
no code implementations • 20 Mar 2020 • Tristan Karch, Cédric Colas, Laetitia Teodorescu, Clément Moulin-Frier, Pierre-Yves Oudeyer
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 learning agent.
2 code implementations • 21 Feb 2020 • Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Ford Dominey, Pierre-Yves Oudeyer
We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning.