AI agents are commonly trained with large datasets of demonstrations of human behavior.However, not all behaviors are equally safe or desirable.Desired characteristics for an AI agent can be expressed by assigning desirability scores, which we assume are assigned to collective trajectories, but not to individual behaviors.For example, in a dataset of vehicle interactions, these scores might relate to the number of incidents that occurred. We first assess the effect of each individual agent's behavior on the collective desirability score, e.g., assessing how likely an agent is to cause incidents.This allows us to afterward only imitate agents with desired behavior, e.g., only imitating agents that are unlikely to cause incidents. To enable this, we propose the concept of an agent's \textit{Exchange Value}, which quantifies an individual agent's contribution to the collective desirability score. This is expressed as the expected change in desirability score when substituting the agent for a randomly selected agent.We propose additional methods for estimating Exchange Values from real-world datasets, enabling us to learn aligned imitation policies that outperform relevant baselines.
Select to Perfect: Imitating desired behavior from large multi-agent data
Tim Franzmeyer Edith Elkind Philip Torr Jakob Nicolaus Foerster Joao F. Henriques