Meta-World+: An Improved, Standardized, RL Benchmark
Reginald McLean ⋅ Evangelos Chatzaroulas ⋅ Luc McCutcheon ⋅ Frank Röder ⋅ Tianhe (Kevin) Yu ⋅ Zhanpeng He ⋅ K.R. Zentner ⋅ Ryan Julian ⋅ Jordan Terry ⋅ Isaac Woungang ⋅ Nariman Farsad ⋅ Pablo Samuel Castro
2025 Poster
in
Workshop: CODEML: Championing Open-source DEvelopment in Machine Learning
in
Workshop: CODEML: Championing Open-source DEvelopment in Machine Learning
Abstract
Multi-task reinforcement learning challenges agents to master diverse skills simultaneously, and Meta-World emerged as the gold standard benchmark for evaluating these algorithms. However, since the introduction of the Meta-World benchmark there have been numerous undocumented changes which inhibit fair comparison of multi-task and meta reinforcement learning algorithms. This work strives to disambiguate these results from the literature, while also producing an open-source version of Meta-World that has full reproducibility of past results.
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