Select to Perfect: Imitating desired behavior from large multi-agent data
arxiv(2024)
摘要
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 not assigned to individual behaviors but to
collective trajectories. 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 selectively imitate agents with a positive effect, e.g., only
imitating agents that are unlikely to cause incidents. To enable this, we
propose the concept of an agent's Exchange Value, which quantifies an
individual agent's contribution to the collective desirability score. The
Exchange Value is 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
desired imitation policies that outperform relevant baselines. The project
website can be found at https://tinyurl.com/select-to-perfect.
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