6 papers
Regime-Conditional Stabilisation of LLM-Augmented Cooperative Multi-Agent Reinforcement Learning
Faid Keddouri, Sohaib Houhou, Aissa Boulmerka +1
Large Language Models (LLMs) offer a natural interface for translating human objectives into reward signals for cooperative multi-agent reinforcement learning (MARL), yet the train…
Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
Kamar Hibatallah Baghdadi, Kawther Guoual Belhamidi, Sara Belhadj +2
We present HiReLC, a hierarchical ensemble-reinforcement learning framework for automated joint quantization and structured pruning of deep neural networks. The framework decompose…
Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See
Mohamed Benabdelouahad, Ahmed Djalal Hacini, Nadir Farhi +1
We investigate how reward design shapes the internal attention patterns of reinforcement learning agents trained for autonomous driving. Using three Perceiver-based agents that sha…
NOVA: Symbolic Regression Discovery of Interpretable Car-Following and Lane-Change Models with Driver Heterogeneity
Ishak Abassi, Nassim Ali Bouazzouni, Farah Ibelaiden +1
We present NOVA, an autonomous symbolic regression framework that identifies interpretable car-following and lane-change structures from raw trajectory data with minimal behavioral…
An LLM-Explainable DRL Framework for Passenger-Directed Autonomous Driving
Ouided Braoui, Meriem Bouali, Nadir Farhi
Autonomous vehicles offer the potential for safer and more efficient mobility, yet public trust remains limited due to the lack of transparency in their decision-making. This work…
R2L: Reliable Reinforcement Learning: Guaranteed Return & Reliable Policies in Reinforcement Learning
Nadir Farhi
In this work, we address the problem of determining reliable policies in reinforcement learning (RL), with a focus on optimization under uncertainty and the need for performance gu…