GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving. - Cillian Brewitt; Balint Gyevnar; Samuel Garcin; Stefano V. Albrecht (IROS, 2021)

A Hybrid Controller for Multi-Agent Collision Avoidance via a Differential Game Formulation. - Domenico Cappello; Samuel Garcin; Z. Mao; Mario Sassano; Aditya A. Paranjape; Thulasi Mylvaganam (IEEE Trans. Control. Syst. Technol., 2021)

Deep reinforcement learning for multi-agent interaction. - Ibrahim H. Ahmed 006; Cillian Brewitt; Ignacio Carlucho; Filippos Christianos; Mhairi Dunion; Elliot Fosong; Samuel Garcin; Shangmin Guo; Balint Gyevnar; Trevor McInroe; Georgios Papoudakis; Arrasy Rahman; et al. (AI Commun., 2022)

How the level sampling process impacts zero-shot generalisation in deep reinforcement learning. - Samuel Garcin; James Doran; Shangmin Guo; Christopher G. Lucas; Stefano V. Albrecht (CoRR, 2023)

DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design. - Samuel Garcin; James Doran; Shangmin Guo; Christopher G. Lucas; Stefano V. Albrecht (ICML, 2024)

ICED: Zero-Shot Transfer in Reinforcement Learning via In-Context Environment Design. - Samuel Garcin; James Doran; Shangmin Guo; Christopher G. Lucas; Stefano V. Albrecht (CoRR, 2024)

Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning. - Samuel Garcin; Trevor McInroe; Pablo Samuel Castro; Christopher G. Lucas; David Abel; Prakash Panangaden; Stefano V. Albrecht (ICLR, 2025)

PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPU. - Trevor McInroe; Samuel Garcin (CoRR, 2025)

MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning. - Tristan Tomilin; Luka van den Boogaard; Samuel Garcin; Bram Grooten; Meng Fang; Mykola Pechenizkiy (CoRR, 2025)