7 papers · 1 filter
Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments
Youcef Mehamlia, Nadir Farhi, Meriem Bouali
Freeway on-ramp merges are major sources of congestion, causing significant economic and environmental costs. While Deep Reinforcement Learning (DRL) offers a promising solution fo…
Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic
Biao Yin, Abderrahmane Kasmi, Nadir Farhi
Connected and automated vehicle (CAV) platooning offers a promising approach to improving road safety and traffic capacity. However, platoon control in real-world traffic is challe…
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…
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…