Leandro Parada — accepted AI-conference papers

2025

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

Leandro Parada, Kevin Yu, Panagiotis Angeloudis

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 4 Source ↗Scholar ↗

2022

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

Eduardo Candela, Leandro Parada, Luis Marques, Tiberiu-Andrei Georgescu, Yiannis Demiris, Panagiotis Angeloudis

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 45 Source ↗Scholar ↗