RL Unplugged

홈페이지 · 논문 7편

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