RLU (RL Unplugged)

Introduced by Gulcehre et al. in RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

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.


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