Meta-World+: An Improved, Standardized, RL Benchmark
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918212980965376 |
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| author | McLean, Reginald Chatzaroulas, Evangelos McCutcheon, Luc Röder, Frank Yu, Tianhe He, Zhanpeng Zentner, K. R. Julian, Ryan Terry, J K Woungang, Isaac Farsad, Nariman Castro, Pablo Samuel |
| author_facet | McLean, Reginald Chatzaroulas, Evangelos McCutcheon, Luc Röder, Frank Yu, Tianhe He, Zhanpeng Zentner, K. R. Julian, Ryan Terry, J K Woungang, Isaac Farsad, Nariman Castro, Pablo Samuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11289 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Meta-World+: An Improved, Standardized, RL Benchmark McLean, Reginald Chatzaroulas, Evangelos McCutcheon, Luc Röder, Frank Yu, Tianhe He, Zhanpeng Zentner, K. R. Julian, Ryan Terry, J K Woungang, Isaac Farsad, Nariman Castro, Pablo Samuel Artificial Intelligence Machine Learning 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. |
| title | Meta-World+: An Improved, Standardized, RL Benchmark |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.11289 |