Publications

CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning

Prashant Govindarajan, Mathieu Reymond, Antoine Clavaud, Mariano Phielipp, Santiago Miret, Sarath Chandar
AI for Accelerated Materials Design Workshop @ ICLR 2025 (to appear)
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A Generalist Hanabi Agent

Arjun V Sudhakar, Hadi Nekoei, Mathieu Reymond, Miao Liu, Janarthanan Rajendran, Sarath Chandar
ICLR 2025 (to appear)
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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 (to appear)
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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
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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 Systems with Applications 2024
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Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs

Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers
TMLR 2023
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WAE-PCN: Wasserstein-autoencoded Pareto Conditioned Networks

Mathieu Reymond*, Florent Delgrange*, Ann Nowé, Guillermo A. Pérez
Adaptive and Learning Agents Workshop @ AAMAS 2023
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Actor-critic multi-objective reinforcement learning for non-linear utility functions

Mathieu Reymond, Conor F. Hayes, Diederik M. Roijers, Denis Steckelmacher, Ann Nowé
Autonomous Agents and Multi-Agent Systems 2023
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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
Autonomous Agents and Multi-Agent Systems 2023
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Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs

Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers
European Workshop on Reinforcement Learning 2022
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Near On-Policy Experience Sampling in Multi-Objective Reinforcement Learning

Shang Wang, Mathieu Reymond, Athirai A. Irissappane, Diederik M. Roijers
AAMAS 2022
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Pareto Conditioned Networks

Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé
AAMAS 2022
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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 Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel De Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers
Autonomous Agents and Multi-Agent Systems
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Actor-Critic Multi-Objective Reinforcement Learning for Non-Linear Utility Functions

Mathieu Reymond, Conor F. Hayes, Diederik M. Roijers, Denis Steckelmacher, Ann Nowé
Multi-Objective Decision Making Workshop 2021
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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
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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 2020
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Pareto-DQN: Approximating the Pareto front in complex multi-objective decision problems

Mathieu Reymond, Ann Nowé
Adaptive and Learning Agents Workshop @ AAMAS 2019
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Reinforcement Learning for Demand Response of Domestic Household Appliances

Mathieu Reymond*, Christophe Patyn*, Roxana Radulescu, Ann Nowé, Geert Deconinck
Adaptive Learning Agents Workshop @ AAMAS 2018
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