Abstract: The performance of imitation learning is typically upper-bounded by the performance of the demonstrator. Recent empirical results show that imitation learning via ranked demonstrations allows for better-than-demonstrator performance; however, ranked demonstrations may be difficult to obtain, and little is known theoretically about when such methods can be expected to outperform the demonstrator. To address these issues, we first contribute a sufficient condition for when better-than-demonstrator performance is possible and discuss why ranked demonstrations can contribute to better-than-demonstrator performance. Building on this theory, we then introduce Disturbance-based Reward Extrapolation (D-REX), a ranking-based imitation learning method that injects noise into a policy learned through behavioral cloning to automatically generate ranked demonstrations. By generating rankings automatically, ranking-based imitation learning can be applied in traditional imitation learning settings where only unlabeled demonstrations are available. We empirically validate our approach on standard MuJoCo and Atari benchmarks and show that D-REX can utilize automatic rankings to significantly surpass the performance of the demonstrator and outperform standard imitation learning approaches. D-REX is the first imitation learning approach to achieve significant extrapolation beyond the demonstrator’s performance without additional side-information or supervision, such as rewards or human preferences.
Keywords: Imitation learning, Reward learning, Ranked demonstrations