Metaheuristics-based Exploration Strategies for Multi-Objective Reinforcement Learning. - Florian Felten; Grégoire Danoy; El-Ghazali Talbi; Pascal Bouvry (ICAART (2), 2022)

A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning. - Florian Felten; Lucas N. Alegre; Ann Nowé; Ana L. C. Bazzan; El-Ghazali Talbi; Grégoire Danoy; Bruno C. da Silva 001 (NeurIPS, 2023)

Hyperparameter Optimization for Multi-Objective Reinforcement Learning. - Florian Felten; Daniel Gareev; El-Ghazali Talbi; Grégoire Danoy (CoRR, 2023)

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning. - Shengyi Huang; Quentin Gallouédec; Florian Felten; Antonin Raffin; Rousslan Fernand Julien Dossa; Yanxiao Zhao; Ryan Sullivan; Viktor Makoviychuk; Denys Makoviichuk; Mohamad H. Danesh; Cyril Roumégous; Jiayi Weng; et al. (CoRR, 2024)

MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning. - Florian Felten; Umut Ucak; Hicham Azmani; Gao Peng; Willem Röpke; Hendrik Baier; Patrick Mannion; Diederik M. Roijers; Jordan K. Terry; El-Ghazali Talbi; Grégoire Danoy; Ann Nowé; et al. (CoRR, 2024)

Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework. - Florian Felten; El-Ghazali Talbi; Grégoire Danoy (J. Artif. Intell. Res., 2024)