Datasets › NeoRL

NeoRL

NeoRL is a collection of environments and datasets for offline reinforcement learning with a special focus on real-world applications. The design follows real-world properties like the conservative of behavior policies, limited amounts of data, high-dimensional state and action spaces, and the highly stochastic nature of the environments.
The datasets include robotics, industrial control, finance trading and city management tasks with real-world properties, containing three-level sizes of dataset, three-level quality of data to mimic the dataset we will meet in offline RL scenarios.
Users can use the dataset to evaluate offline RL algorithms with near real-world application nature.

Benchmarks archive 2025-07-28

No leaderboard in the archive resolves to this dataset.

No paper in the archive has a leaderboard row on this dataset; the archive counts 10 papers for it but never published that list.

No loader listed in the archive.

1 variant name, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections