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Maxime Toquebiau
- PhD Student
- Team: ACIDE
- Email: maxime.toquebiau@ece.fr
- Phone:0642538424
- Bio: Engineer specialised in artificial intelligence, I graduated in 2020 from ECE Paris. My PhD thesis subject is "Multiagent deep reinforcement learning in mobile robotics", supervised by Faïz BEN AMAR (ISIR, co-director), Nicolas BREDECHE (ISIR, co-director) and Jae Yun JUN KIM (ECE Paris, supervisor).
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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