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

Keywords: multi-task reinforcement learning, meta-reinforcement learning

TL;DR: Undocumented versions of Meta-World have clouded algorithmic performance. This work strives to disambiguate Meta-World results from the literature, while also providing insights into benchmark design.

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.

Submission Number: 11