Meta-World+: An Improved, Standardized, RL Benchmark
Abstract
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 an open-source version of Meta-World 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.
Cite
Text
McLean et al. "Meta-World+: An Improved, Standardized, RL Benchmark." Advances in Neural Information Processing Systems, 2025.Markdown
[McLean et al. "Meta-World+: An Improved, Standardized, RL Benchmark." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/mclean2025neurips-metaworld/)BibTeX
@inproceedings{mclean2025neurips-metaworld,
title = {{Meta-World+: An Improved, Standardized, RL Benchmark}},
author = {McLean, Reginald and Chatzaroulas, Evangelos and McCutcheon, Luc and Röder, Frank and Yu, Tianhe and He, Zhanpeng and Zentner, K.R. and Julian, Ryan and Terry, J K and Woungang, Isaac and Farsad, Nariman and Castro, Pablo Samuel},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/mclean2025neurips-metaworld/}
}