Meta-World+: An Improved, Standardized, RL Benchmark
algorithm comparisonbenchmark designdiverse skillsevaluation frameworkliterature disambiguationmeta-reinforcement learningmeta-worldmulti-task learningopen-sourcereproducibilityskill masterytask controltask settechnical ergonomicsundocumented changes
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.