1 code implementation • 20 May 2021 • Mathieu Seurin, Florian Strub, Philippe Preux, Olivier Pietquin
Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize.
no code implementations • ICLR Workshop SSL-RL 2021 • Mathieu Seurin, Florian Strub, Philippe Preux, Olivier Pietquin
We evaluate RAM on the procedurally-generated environment MiniGrid, against state-of-the-art methods.
no code implementations • 7 Aug 2020 • Mathieu Seurin, Florian Strub, Philippe Preux, Olivier Pietquin
To do so, we cast the speaker recognition task into a sequential decision-making problem that we solve with Reinforcement Learning.
no code implementations • 21 Oct 2019 • Geoffrey Cideron, Mathieu Seurin, Florian Strub, Olivier Pietquin
Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality.
no code implementations • 4 Oct 2019 • Mathieu Seurin, Philippe Preux, Olivier Pietquin
Violating constraints thus results in rejected actions or entering in a safe mode driven by an external controller, making RL agents incapable of learning from their mistakes.
no code implementations • 25 Sep 2019 • Geoffrey Cideron, Mathieu Seurin, Florian Strub, Olivier Pietquin
Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality.
1 code implementation • ECCV 2018 • Florian Strub, Mathieu Seurin, Ethan Perez, Harm de Vries, Jérémie Mary, Philippe Preux, Aaron Courville, Olivier Pietquin
Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue.
no code implementations • 15 Sep 2017 • Timothée Lesort, Mathieu Seurin, Xinrui Li, Natalia Díaz Rodríguez, David Filliat
We reproduce this simplification process using a neural network to build a low dimensional state representation of the world from images acquired by a robot.