Supervised Reward Inference

Existing approaches to reward inference from behavior typically assume that humans provide demonstrations according to specific models of behavior. However, humans often indicate their goals through a wide range of behaviors, from actions that are suboptimal due to poor planning or execution to behaviors which are intended to communicate goals rather than achieve them. We propose that supervised learning offers a unified framework to infer reward functions from any class of behavior, and show that such an approach is asymptotically Bayes-optimal under mild assumptions. Experiments on simulated robotic manipulation tasks show that our method can efficiently infer rewards from a wide variety of arbitrarily suboptimal demonstrations.

Value Alignment Verification

As humans interact with autonomous agents to perform increasingly complicated, potentially risky tasks, it is important that humans can verify these agents' trustworthiness and efficiently evaluate their performance and correctness. In this paper we formalize the problem of value alignment verification: how to efficiently test whether the goals and behavior of another agent are aligned with a human's values? We explore several different value alignment verification settings and provide foundational theory regarding value alignment verification. We study alignment verification problems with an idealized human that has an explicit reward function as well as value alignment verification problems where the human has implicit values. Our theoretical and empirical results in both a discrete grid navigation domain and a continuous autonomous driving domain demonstrate that it is possible to synthesize highly efficient and accurate value alignment verification tests for certifying the alignment of autonomous agents.