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Maxime Toquebiau
- Doctorant
- Équipe: ACIDE
- Email: maxime.toquebiau@ece.fr
- Telephone:0642538424
- Bio: Ingénieur spécialisé en intelligence artificielle, diplômé de l'ECE Paris en 2020. Je fais ma thèse sur le sujet "Multiagent deep reinforcement learning in mobile robotics", supervisé par Faïz BEN AMAR (ISIR, co-directeur), Nicolas BREDECHE (ISIR, co-directeur) et Jae Yun JUN KIM (ECE Paris, encadrant).
Publications
- Maxime Toquebiau, Jae-Yun Jun, Faïz Benamar, Nicolas Bredeche. Multi-Agent Deep Reinforcement Learning in Robotics: Context and Open Challenges. 2025. ⟨hal-05400696⟩
- Maxime Toquebiau, Jae-Yun Jun, Faïz Benamar, Nicolas Bredeche. An Introduction to Deep Reinforcement Learning. Sorbonne universités. 2025. ⟨hal-05400685⟩
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Maxime Toquebiau, Jae-Yun Jun, Faïz Benamar, Nicolas Bredeche. Towards Language-Augmented Multi-Agent Deep Reinforcement Learning. The European Conference on Artificial Intelligence, Oct 2025, Bologna (ITALY), Italy. pp.3791 - 3798, ⟨10.3233/faia251260⟩. ⟨hal-05734607⟩
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- Maxime Toquebiau. Improving Cooperation in Multi-agent Deep Reinforcement Learning for Mobile Robotics. Robotics [cs.RO]. Sorbonne Université, 2024. English. ⟨NNT : 2024SORUS385⟩. ⟨tel-05076852⟩
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Maxime Toquebiau, Nicolas Bredeche, Faïz Benamar, Jae-Yun Jun. Joint Intrinsic Motivation for Coordinated Exploration in Multi-Agent Deep Reinforcement Learning. The 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2024, AUCKLAND, New Zealand. ⟨hal-05734661⟩
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