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RL Unplugged is suite of benchmarks for offline reinforcement learning. The RL Unplugged is designed around the following considerations: to facilitate ease of use, we provide the datasets 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, we try to focus on the following problems: -High dimensional action spaces, for example the locomotion humanoid domains, we have 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.