Divide & Conquer Imitation Learning

Understand

When cast into the Deep Reinforcement Learning framework, many robotics tasks require solving a long horizon and sparse reward problem, where learning algorithms struggle.

• In such context, Imitation Learning (IL) can be a powerful approach to bootstrap the learning process.
• However, most IL methods require several expert demonstrations which can be prohibitively difficult to acquire.
• Only a handful of IL algorithms have shown efficiency in the context of an extreme low expert data regime where a single expert demonstration is available.