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arXiv · 2505.11289

Meta-World+: An Improved, Standardized, RL Benchmark

Reginald McLean·Evangelos Chatzaroulas·Luc McCutcheon·Frank Röder·Tianhe Yu·Zhanpeng He·K. R. Zentner·Ryan Julian·J K Terry·Isaac Woungang·Nariman Farsad·Pablo Samuel Castro
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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 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.

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Reginald McLean, Evangelos Chatzaroulas, Luc McCutcheon, Frank Röder, Tianhe Yu, Zhanpeng He, K. R. Zentner, Ryan Julian, J K Terry, Isaac Woungang, Nariman Farsad, Pablo Samuel Castro. 2025-05-16. Meta-World+: An Improved, Standardized, RL Benchmark. https://arxiv.org/abs/2505.11289

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