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publications
Multi-task French speech analysis with deep learning Emotion recognition and speaker diarization models for end-to-end conversational analysis tool
Published in Chalmers Open Digital Repository, 2023
This work explores multi-task learning for French speech analysis, combining emotion recognition and speaker diarization models into a single end-to-end conversational analysis tool.
Recommended citation: Sintes, Jules. (2023). "Multi-task French speech analysis with deep learning Emotion recognition and speaker diarization models for end-to-end conversational analysis tool."
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AI challenge for safe and low carbon power grid operation
Published in Energy and AI, 2025
This paper presents an AI challenge designed to foster safe and low-carbon solutions for power grid operation.
Recommended citation: Pavão, Adrien, Marot, Antoine, Sintes, Jules, Möllerstedt, Viktor Eriksson, Crochepierre, Laure, Chaouache, Karim, Donnot, Benjamin, Dang, Van Tuan, & Guyon, Isabelle. (2025). "AI challenge for safe and low carbon power grid operation." Energy and AI, 100564. Elsevier.
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COGNAC: Cooperative Graph-based Networked Agent Challenges for Multi-Agent Reinforcement Learning
Published in Advances in Neural Information Processing Systems, 2025
Many controlled complex systems have an inherent network structure, such as power grids, traffic light systems, or computer networks. Automatically controlling these systems is highly challenging due to their combinatorial complexity. Standard single-agent reinforcement learning (RL) approaches often struggle with the curse of dimensionality in such settings. In contrast, the multi-agent paradigm offers a promising solution by distributing decision-making, thereby addressing both algorithmic and combinatorial challenges. In this paper, we introduce COGNAC (COoperative Graph-based Networked Agent Challenges), a collection of cooperative graph-structured environments designed to facilitate experiments across different graph sizes and topologies. COGNAC bridges the gap between theoretical research in network control and practical multi-agent RL (MARL) applications by offering a flexible, scalable platform with a suite of simple yet highly challenging problems rooted in networked environments. Our benchmarks also support the development and evaluation of decentralized and distributed learning algorithms, motivated by the growing interest in more sustainable and frugal AI systems. Experiments on COGNAC show that independent actor–critic learning (IPPO) yields the highest-quality joint policies while scaling robustly to large network sizes with minimal hyperparameter tuning. Value-based independent learning (IDQL) typically needs substantially more training and is less reliable on combinatorial tasks. In contrast, standard Centralized-Training Decentralized-Execution (CTDE) methods and fully centralized training are slower to converge, less stable, and struggle to generalize to larger, more interdependent networks. These results suggest that CTDE approaches likely need extra information or inter-agent communication to fully capture the underlying network structure of each problem
Recommended citation: Sintes, Jules. (2025). " COGNAC: Cooperative Graph-based Networked Agent Challenges for Multi-Agent Reinforcement Learning "
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