Papers › A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning

A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning

26 Sep 2023Conference on Neural Information Processing Systems Datasets and Benchmarks Track 2023 9archive 2025-07-28

Florian Felten, Lucas N. Alegre, Ann Nowé, Ana L. C. Bazzan, El-Ghazali Talbi, Grégoire Danoy, Bruno C. da Silva

Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement learning (RL) to scenarios where agents must optimize multiple---potentially conflicting---objectives, each represented by a distinct reward function. To facilitate and accelerate research and benchmarking in multi-objective RL problems, we introduce a comprehensive collection of software libraries that includes: (i) MO-Gymnasium, an easy-to-use and flexible API enabling the rapid construction of novel MORL environments. It also includes more than 20 environments under this API. This allows researchers to effortlessly evaluate any algorithms on any existing domains; (ii) MORL-Baselines, a collection of reliable and efficient implementations of state-of-the-art MORL algorithms, designed to provide a solid foundation for advancing research. Notably, all algorithms are inherently compatible with MO-Gymnasium; and (iii) a thorough and robust set of benchmark results and comparisons of MORL-Baselines algorithms, tested across various challenging MO-Gymnasium environments. These benchmarks were constructed to serve as guidelines for the research community, underscoring the properties, advantages, and limitations of each particular state-of-the-art method.

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lucasalegre/morl-baselines officialmentioned in paperpytorchMIT report

Farama-Foundation/MO-Gymnasium mentioned in paper report

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Benchmarking Multi-Objective Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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