Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling

Yuping Luo, Huazhe Xu, Tengyu Ma

Abstract: We introduce a notion of conservatively-extrapolated value functions, which provably lead to policies that can self-correct to stay close to the demonstration states, and learn them with a novel negative sampling technique.