Meta-World is widely used for evaluating multi-task and meta-reinforcement learning agents, which are challenged to master diverse skills simultaneously. Since its introduction however, there have been numerous undocumented changes which inhibit a fair comparison of algorithms. This work strives to disambiguate these results from the literature, while also leveraging the past versions of Meta-World to provide insights into multi-task and meta-reinforcement learning benchmark design. Through this process we release a new open-source version of Meta-World (https://github.com/Farama-Foundation/Metaworld/) that has full reproducibility of past results, is more technically ergonomic, and gives users more control over the tasks that are included in a task set.
@article{arxiv.2505.11289,
title = {Meta-World+: An Improved, Standardized, RL Benchmark},
author = {Reginald McLean and Evangelos Chatzaroulas and Luc McCutcheon and Frank Röder and Tianhe Yu and Zhanpeng He and K. R. Zentner and Ryan Julian and J K Terry and Isaac Woungang and Nariman Farsad and Pablo Samuel Castro},
journal= {arXiv preprint arXiv:2505.11289},
year = {2025}
}