Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models. - Diederik M. Roijers; Luisa M. Zintgraf; Pieter Libin; Mathieu Reymond; Eugenio Bargiacchi; Ann Nowé (ECML/PKDD (3), 2020)

Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning. - Conor F. Hayes; Mathieu Reymond; Diederik M. Roijers; Enda Howley; Patrick Mannion (AAMAS, 2021)

Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search. - Conor F. Hayes; Mathieu Reymond; Diederik M. Roijers; Enda Howley; Patrick Mannion (CoRR, 2021)

Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. - Raphaël Avalos; Mathieu Reymond; Ann Nowé; Diederik M. Roijers (AAMAS, 2022)

Pareto Conditioned Networks. - Mathieu Reymond; Eugenio Bargiacchi; Ann Nowé (AAMAS, 2022)

Near On-Policy Experience Sampling in Multi-Objective Reinforcement Learning. - Shang Wang; Mathieu Reymond; Athirai A. Irissappane; Diederik M. Roijers (AAMAS, 2022)

A practical guide to multi-objective reinforcement learning and planning. - Conor F. Hayes; Roxana Radulescu; Eugenio Bargiacchi; Johan Källström; Matthew Macfarlane; Mathieu Reymond; Timothy Verstraeten; Luisa M. Zintgraf; Richard Dazeley; Fredrik Heintz; Enda Howley; Athirai A. Irissappane; et al. (Auton. Agents Multi Agent Syst., 2022)

A Brief Guide to Multi-Objective Reinforcement Learning and Planning. - Conor F. Hayes; Roxana Radulescu; Eugenio Bargiacchi; Johan Källström; Matthew Macfarlane; Mathieu Reymond; Timothy Verstraeten; Luisa M. Zintgraf; Richard Dazeley; Fredrik Heintz; Enda Howley; Athirai A. Irissappane; et al. (AAMAS, 2023)

Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs. - Raphaël Avalos; Mathieu Reymond; Ann Nowé; Diederik M. Roijers (Trans. Mach. Learn. Res., 2023)

Monte Carlo tree search algorithms for risk-aware and multi-objective reinforcement learning. - Conor F. Hayes; Mathieu Reymond; Diederik M. Roijers; Enda Howley; Patrick Mannion (Auton. Agents Multi Agent Syst., 2023)

Actor-critic multi-objective reinforcement learning for non-linear utility functions. - Mathieu Reymond; Conor F. Hayes; Denis Steckelmacher; Diederik M. Roijers; Ann Nowé (Auton. Agents Multi Agent Syst., 2023)

Interactively Learning the User's Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits. - Mathieu Reymond; Eugenio Bargiacchi; Diederik M. Roijers; Ann Nowé (AAMAS, 2024)

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning. - Mathieu Reymond; Conor F. Hayes; Lander Willem; Roxana Radulescu; Steven Abrams; Diederik M. Roijers; Enda Howley; Patrick Mannion; Niel Hens; Ann Nowé; Pieter Libin (Expert Syst. Appl., 2024)

A Generalist Hanabi Agent. - Arjun Vaithilingam Sudhakar; Hadi Nekoei; Mathieu Reymond; Miao Liu; Janarthanan Rajendran; Sarath Chandar (ICLR, 2025)

Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning. - Willem Röpke; Mathieu Reymond; Patrick Mannion; Diederik M. Roijers; Ann Nowé; Roxana Radulescu (AAMAS, 2025)