RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

Understand

Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications.

• They make it possible to learn policies from offline datasets, thus overcoming concerns associated with online data collection in the real-world, including cost, safety, or ethical concerns.
• In this paper, we propose a benchmark called RL Unplugged to evaluate and compare offline RL methods.
• RL Unplugged includes data from a diverse range of domains including games (e.g., Atari benchmark) and simulated motor control problems (e.g., DM Control Suite).