Offline Reinforcement Learning - What's Next?

In my group at Berkeley, we're focusing a lot on what we call offline reinforcement learning algorithms. And the idea is that traditionally reinforcement learning is thought of as a very online and interactive learning regime. The most successful large-scale machine learning systems train on datasets that are stored to disk and then reused repeatedly because they don't want to recollect those interactively each time you retrain your system. That's the premise behind offline reinforcement learning.