Mathieu Reymond

According to our database, Mathieu Reymond authored at least 12 papers between 2020 and 2024.

Collaborative distances:

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

2024

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

CoRR, 2024

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., October, 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., October, 2023

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, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

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

CoRR, 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, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers

Auton. Agents Multi Agent Syst., 2022

Near On-Policy Experience Sampling in Multi-Objective Reinforcement Learning.

Shang Wang, Mathieu Reymond, Athirai A. Irissappane, Diederik M. Roijers

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

Pareto Conditioned Networks.

Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning.

Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

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

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

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020

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

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020