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Meta-World is an open-source simulation benchmark for **multi-task and meta reinforcement learning** in continuous-control robotic manipulation. It bundles 50 diverse manipulation tasks using everyday objects and a common tabletop Sawyer arm, providing a standardized playground to test whether algorithms can learn many different tasks and generalize quickly to new ones.
- Paper: [Meta-World+: An Improved, Standardized, RL Benchmark](https://arxiv.org/abs/1910.10897)
- Paper: [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning paper](https://arxiv.org/abs/1910.10897)
- GitHub: [Farama-Foundation/Metaworld](https://github.com/Farama-Foundation/Metaworld)
- Project website: [metaworld.farama.org](https://metaworld.farama.org)