Work

Meta-world+: an improved, standardized, RL benchmark

Year: 2025

Type: article

Abstract: 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-World1 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.

Source: TUHH Open Research

DOI: 10.15480/882.16723

Authors: McLean, Reginald, Chatzaroulas, Evangelos, McCutcheon, Luc, Röder, Frank, He, Zhanpeng, Yu, Tianhe, Julian, Ryan, Zentner, K. R., Terry, Jordan, Woungang, Isaac, Farsad, Nariman, Castro, Pablo Samuel

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7129737147

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