Select to Perfect: Imitating desired behavior from large multi-agent data
This addresses safety and desirability in AI training for applications like autonomous driving, but it is incremental as it builds on existing imitation learning with a new selection criterion.
The paper tackles the problem of training AI agents from large multi-agent datasets by selectively imitating agents that positively impact collective desirability scores, such as reducing incidents in vehicle interactions, and demonstrates that their method outperforms baselines.
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.