Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution
Training AI systems that juggle multiple competing goals efficiently
When AI systems must balance conflicting objectives—like being both helpful and honest, or fast and energy-efficient—training becomes inefficient because the best policy changes dramatically depending on how much you weight each goal. This paper measures how much objectives actually conflict and introduces two techniques to learn faster: maintaining a small team of specialist policies that each excel at different goal combinations, and identifying which training examples help or hurt learning on new goals. The methods improved performance by 4–27% across robotic control and other benchmarks.
Most real AI systems serve multiple purposes that pull in opposite directions. These techniques could let engineers train aligned AI systems and robots more efficiently, reducing computational cost and time—critical as AI systems grow larger and more complex.