Robust Imitation of Diverse Behaviors

Understand

Deep generative models have recently shown great promise in imitation learning for motor control.

• Given enough data, even supervised approaches can do one-shot imitation learning; however, they are vulnerable to cascading failures when the agent trajectory diverges from the demonstrations.
• Compared to purely supervised methods, Generative Adversarial Imitation Learning (GAIL) can learn more robust controllers from fewer demonstrations, but is inherently mode-seeking and more difficult to train.
• In this paper, we show how to combine the favourable aspects of these two approaches.