In real-world applications, event data is typically received in a streaming manner, where the distribution of patterns may shift over time. Additionally, privacy and memory constraints are commonly observed in practical scenarios, further compounding the challenges.

In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.

The robot could learn from these demonstrations to complete the tasks autonomously. For IL algorithms to be useful, however, they must be able to learn how to perform tasks from few demonstrations. A domestic robot wouldn't be very helpful if it required thirty demonstrations before it figured out that you are deliberately washing your purple cravat