Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Real. - Eduardo Candela; Leandro Parada; Luís Marques 002; Tiberiu-Andrei Georgescu; Yiannis Demiris; Panagiotis Angeloudis (IROS, 2022)

A Decentralised Multi-Agent Reinforcement Learning Approach for the Same-Day Delivery Problem. - Elvin Ngu; Leandro Parada; Jose Javier Escribano Macias; Panagiotis Angeloudis (CoRR, 2022)

Safe and Efficient Manoeuvring for Emergency Vehicles in Autonomous Traffic using Multi-Agent Proximal Policy Optimisation. - Leandro Parada; Eduardo Candela; Luís Marques 002; Panagiotis Angeloudis (CoRR, 2022)

An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios. - Leandro Parada; Hanlin Tian; Jose Escribano Macias; Panagiotis Angeloudis (ITSC, 2023)

Risk-aware controller for autonomous vehicles using model-based collision prediction and reinforcement learning. - Eduardo Candela; Olivier Doustaly; Leandro Parada; Felix Feng; Yiannis Demiris; Panagiotis Angeloudis (Artif. Intell., 2023)

A Federated Learning-Based Framework for Ride-Sourcing Traffic Demand Prediction. - Simon Hu 001; Yin Ye; Qinru Hu; Xin Liu; Shaosheng Cao; Howard H. Yang; Yongdong Shen; Panagiotis Angeloudis; Leandro Parada; Chao Wu 001 (IEEE Trans. Veh. Technol., 2023)

An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios. - Leandro Parada; Hanlin Tian; José Escribano; Panagiotis Angeloudis (CoRR, 2024)

IntNet: A Communication-Driven Multi-Agent Reinforcement Learning Framework for Cooperative Autonomous Driving. - Leandro Parada; Kevin Yu; Panagiotis Angeloudis (IEEE Robotics Autom. Lett., 2025)