122 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Institut national de recherche en sciences et technologies du numériqueFR3 papers
- Institut Systèmes Intelligents et de RobotiqueFR3 papers
- Sorbonne UniversitéFR3 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- Microsoft (United States)US2 papers
- Centre Inria de l'université de BordeauxFR1 paper
- École Nationale Supérieure de Techniques AvancéesFR1 paper
- École Nationale Supérieure de Techniques Avancées ParisFR1 paper
- École PolytechniqueFR1 paper
- École Pour l'Informatique et les Techniques AvancéesFR1 paper
- Evidence Based Research (United States)US1 paper
- Flower HospitalUS1 paper
9 papers
GPT-3-driven pedagogical agents for training children's curious question-asking skills
Rania Abdelghani, Yen-Hsiang Wang, Xingdi Yuan +4
In order to train children's ability to ask curiosity-driven questions, previous research has explored designing specific exercises relying on providing semantic and linguistic cue…
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL
Thomas Carta, Pierre-Yves Oudeyer, Olivier Sigaud +1
Reinforcement learning (RL) in long horizon and sparse reward tasks is notoriously difficult and requires a lot of training steps. A standard solution to speed up the process is to…
Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language
Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté +2
To solve difficult tasks, humans ask questions to acquire knowledge from external sources. In contrast, classical reinforcement learning agents lack such an ability and often resor…
Conversational agents for fostering curiosity-driven learning in children
Rania Abdelghani, Pierre-Yves Oudeyer, Edith Law +2
Curiosity is an important factor that favors independent and individualized learning in children. Research suggests that it is also a competence that can be fostered by training sp…
Questioning causality on sex, gender and COVID-19, and identifying bias in large-scale data-driven analyses: the Bias Priority Recommendations and Bias Catalog for Pandemics
Natalia Díaz-Rodríguez, Rūta Binkytė-Sadauskienė, Wafae Bakkali +4
The COVID-19 pandemic has spurred a large amount of observational studies reporting linkages between the risk of developing severe COVID-19 or dying from it, and sex and gender. By…
SocialAI 0.1: Towards a Benchmark to Stimulate Research on Socio-Cognitive Abilities in Deep Reinforcement Learning Agents
Grgur Kovač, Rémy Portelas, Katja Hofmann +1
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. This problem motivated many research directions…
TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
Clément Romac, Rémy Portelas, Katja Hofmann +1
Training autonomous agents able to generalize to multiple tasks is a key target of Deep Reinforcement Learning (DRL) research. In parallel to improving DRL algorithms themselves, A…
GRIMGEP: Learning Progress for Robust Goal Sampling in Visual Deep Reinforcement Learning
Grgur Kovač, Adrien Laversanne-Finot, Pierre-Yves Oudeyer
Designing agents, capable of learning autonomously a wide range of skills is critical in order to increase the scope of reinforcement learning. It will both increase the diversity…
Identification of Invariant Sensorimotor Structures as a Prerequisite for the Discovery of Objects
Nicolas Le Hir, Olivier Sigaud, Alban Laflaquière
Perceiving the surrounding environment in terms of objects is useful for any general purpose intelligent agent. In this paper, we investigate a fundamental mechanism making object…