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cs.AI2025
TDHook: A Lightweight Framework for Interpretability
Yoann Poupart
Interpretability of Deep Neural Networks (DNNs) is a growing field driven by the study of vision and language models. Yet, some use cases, like image captioning, or domains like De…
cs.AI2025
Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning
Yoann Poupart, Aurélie Beynier, Nicolas Maudet
Multi-Agent Deep Reinforcement Learning (MADRL) was proven efficient in solving complex problems in robotics or games, yet most of the trained models are hard to interpret. While l…