Samuel Garcin

According to our database, Samuel Garcin authored at least 9 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, June, 2025

PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPU.

Trevor McInroe, Samuel Garcin

CoRR, February, 2025

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design.

Samuel Garcin, James Doran, Shangmin Guo, Christopher G. Lucas, Stefano V. Albrecht

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

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

2022

Deep reinforcement learning for multi-agent interaction.

Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho, Filippos Christianos, Mhairi Dunion, Elliot Fosong, Samuel Garcin, Shangmin Guo, Balint Gyevnar, Trevor McInroe, Georgios Papoudakis, Arrasy Rahman, Lukas Schäfer, Massimiliano Tamborski, Giuseppe Vecchio, Cheng Wang, Stefano V. Albrecht

AI Commun., 2022

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

GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving.

Cillian Brewitt, Balint Gyevnar, Samuel Garcin, Stefano V. Albrecht

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021