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From an AI and machine learning perspective, we study how machine can efficiently acquire world models, as well as open-ended repertoires of skills over an extended time span. In particular, we develop deep reinforcement learning agents able to learn to represent and generate their own goals, leveraging grounded language learning for out-of-distribution creative exploration (see this blog post), and automatic curriculum learning. This addresses the major AI challenge of how to learn autonomously in high-dimensional environments, when there are no external rewards and many potential distractors. We are combining these approaches with self-supervised deep learning techniques, used to learn spaces in which to self-generate goals, to discover independently controllable features, solve efficiently sparse reward problems in Deep RL, and learn efficiently modular goal-conditioned policies. We also study how neuro-symbolic architectures can enable learning structured representations and handling systematic compositionality and generalization. Recently, we started exploring the new area of automated discovery of self-organized patterns in complex systems, leveraging intrinsically motivated goal exploration and unsupervised representation learning.
I also work on theoretical models of the origins and evolution of speech and language, studying the role of self-organization in neural networks and agents dynamical coupling. In the new edition of my book “Self-organization in the evolution of speech” (to appear in 2020 at OUP, CC-BY), I present an integrated view of the roles of self-organization and intrinsic motivation in the origins of language.
I also work on theoretical models of the origins and evolution of speech and language, studying the role of self-organization in neural networks and agents dynamical coupling. In the new edition of my book “Self-organization in the evolution of speech” (to appear in 2020 at OUP, CC-BY), I present an integrated view of the roles of self-organization and intrinsic motivation in the origins of language.
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Pour la Scienceno. 3 (2024): 24-31
Nature Machine Intelligenceno. 1 (2024): 6-12
arxiv(2023)
22ND ANNUAL ACM INTERACTION DESIGN AND CHILDREN CONFERENCE, IDC 2023: Rediscovering Childhoodpp.495-501, (2023)
Erwan Plantec,Gautier Hamon,Mayalen Etcheverry,Pierre-Yves Oudeyer,Clément Moulin-Frier, Bert Wang-Chak Chan
arxiv(2023)
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