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.

Meta-RL with self-supervised predictive coding modules can learn interpretable, task-relevant representations that better approximate Bayes-optimal belief states than black-box meta-RL models across diverse partially observable environments.