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Panagiotis Angeloudis

3 accepted papers

2025

IntNet: A Communication-Driven Multi-Agent Reinforcement Learning Framework for Cooperative Autonomous Driving

RA-L 2025

Achieving safety in autonomous driving through Multi-Agent Reinforcement Learning (MARL) is a critical yet challenging task due to non-stationarity, partial observability, and the need for effective coordination among agents. Although earlier cooperative MARL methods have aimed to improve coordinati

Cited by 4SourceScholar
2024

Bezier Everywhere All at Once: Learning Drivable Lanes as Bezier Graphs

CVPR 2024poster

Knowledge of lane topology is a core problem in autonomous driving. Aerial imagery can provide high resolution quickly updatable lane source data but detecting lanes from such data has so far been an expensive manual process or where automated solutions exist undrivable and requiring of downstream p…

2022

Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Real

IROS 2022poster

Autonomous Driving requires high levels of coordination and collaboration between agents. Achieving effective coordination in multi-agent systems is a difficult task that remains largely unresolved. Multi-Agent Reinforcement Learning has arisen as a powerful method to accomplish this task because it…

Cited by 45SourceScholar