Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information

Understand

The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging.

• In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning.
• In this work, we discover the interaction between sub-tasks from their resulting state-action trajectory sequences using a directed graphical model.
• We propose a new algorithm based on the generative adversarial imitation learning framework which automatically learns sub-task policies from unsegmented demonstrations.