NeoRL
Introduced by Qin et al. in NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
- 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.
Papers
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