RL Unplugged is suite of benchmarks for offline reinforcement learning. The RL Unplugged is designed around the following considerations: to facilitate ease of use, the datasets are provided with a unified API which makes it easy for the practitioner to work with all data in the suite once a general pipeline has been established. This is a dataset accompanying the paper RL Unplugged: Benchmarks for Offline Reinforcement Learning.
In this suite of benchmarks, the authors try to focus on the following problems:
- High dimensional action spaces, for example the locomotion humanoid domains, there are 56 dimensional actions.
- High dimensional observations.
- Partial observability, observations have egocentric vision.
- Difficulty of exploration, using states of the art algorithms and imitation to generate data for difficult environments.
- Real world challenges.
Source: DeepMind
