Burn-In Demonstrations for Multi-Modal Imitation Learning

Understand

Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data.

• However, past approaches have focused only on recreating a small number of distinct, expert maneuvers, or have relied on supervised learning techniques that produce unstable policies.
• This work extends InfoGAIL, an algorithm for multi-modal imitation learning, to reproduce behavior over an extended period of time.
• Our approach involves reformulating the typical imitation learning setting to include "burn-in demonstrations" upon which policies are conditioned at test time.