# Multi-Objective Reinforcement Learning with Routed Ensembles and Trajectory Attribution

- Source: arXiv cs.RO
- Published: 2026-10-27T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/s4j5trqjlu5s3cq202lnm5y6f
- Original: https://arxiv.org/abs/2610.35733

## Summary

This paper studies policy learning in multi-objective reinforcement learning, proposing objective-conflict coefficients and using them to derive near-optimal regret bounds for online settings, quantifying the impact of objective misalignment on learning efficiency. The authors design a routed ensemble policy learning algorithm that maintains a small set of basis policies and dynamically routes trajectories to the policy with the highest potential reward; they also propose a kernel surrogate trajectory attribution algorithm that estimates how trajectory feedback signals affect the policy learning of other trajectories.